Systems, devices, and methods for alleviating glucotoxicity and restoring pancreatic beta-cell function in advanced diabetes mellitus

ABSTRACT

Systems, methods and/or devices for treating diabetes mellitus by alleviating glucotoxicity and restoring pancreatic beta-cell function, comprising at least a first memory for storing data inputs corresponding at least to one or more components in a patient&#39;s present insulin dosage regimen, and data inputs corresponding at least to the patient&#39;s blood-glucose-level measurements determined at a plurality of times, and a processor operatively connected to the at least first memory. The processor is programmed at least to determine from the data inputs corresponding to the patient&#39;s blood-glucose-level measurements determined at a plurality of times whether and by how much to vary at least one of the components of the patient&#39;s present insulin dosage regimen. Also disclosed are systems, methods, and/or devices for alleviating glucotoxicity and restoring pancreatic beta-cell function, comprising establishing the patient&#39;s current glycemic state relative to a desired glycemic range and determining from at least one of a plurality of the data corresponding to the patient&#39;s blood glucose-level measurements whether and by how much to adjust at least one of the components in the patient&#39;s present insulin dosage regimen.

RELATED DOCUMENTS

This application is a continuation of U.S. application Ser. No.14/452,140, filed Aug. 5, 2014, which is related to, and claims thebenefit of priority from, U.S. Provisional Application Ser. No.61/862,327 filed Aug. 5, 2013. This application is also acontinuation-in-part of U.S. patent application Ser. No. 13/168,659,filed Jun. 24, 2011, which issued as U.S. Pat. No. 9,220,456 on Dec. 29,2015; which was a continuation-in-part of U.S. patent application Ser.No. 12/417,955, filed Apr. 3, 2009, which issued as U.S. Pat. No.8,600,682 on Dec. 3, 2014 and U.S. patent application Ser. No.12/417,960, filed Apr. 3, 2009, which issued as U.S. Pat. No. 8,457,901on Jun. 4, 2013; both of which claimed priority to U.S. ProvisionalApplication Ser. No. 61/042,487, filed on Apr. 4, 2008 and U.S.Provisional Application Ser. No. 61/060,645, filed on Jun. 11, 2008.Each of these applications and patents is herein incorporated byreference in its entirety.

In addition, the present application is related to PCT/US2009/039421,filed Apr. 3, 2009; PCT/US2009/039418, filed Apr. 3, 2009; U.S.Provisional Application Ser. No. 61/113,252, filed Nov. 11, 2008; U.S.Provisional Application Ser. No. 61/257,866, filed Nov. 4, 2009;PCT/US2009/063989, filed Nov. 11, 2009; U.S. Provisional ApplicationSer. No. 61/257,886 filed Nov. 4, 2009; U.S. patent application Ser. No.12/926,234, filed Nov. 3, 2010; and PCT/US2010/055246, filed Nov. 3,2010. Each of these applications and patents is incorporated herein byreference in its entirety. Finally, the reference “Convex Optimization”by Boyd and Vandenberghe (Cambridge University Press, 2004; ISBN-10:0521833787), is hereby incorporated by reference in its entirety.

FIELD

The present disclosure relates to systems, methods and/or devices forachieving glycemic balance in a diabetes patient, and more particularlyto such systems, methods and/or devices according to which a processoris programmed at least to determine from the data inputs correspondingto the patient's blood-glucose-level measurements determined at aplurality of times whether and by how much to vary at least one of theone or more components in the patient's present insulin dosage regimenin order to achieve durable glycemic balance, thereby alleviatingglucotoxicity and/or restoring pancreatic beta-cell function.

BACKGROUND

Diabetes mellitus (“DM” or “diabetes”) is a chronic disease resultingfrom deficient insulin secretion by the beta cells of the endocrinepancreas. About 7% of the general population in the Western Hemispheresuffers from diabetes. Of these persons, roughly 90% suffer from Type-2diabetes while approximately 10% suffer from Type-1. In Type-1 diabetes,patients effectively surrender their endocrine pancreas to autoimmunedistraction and so become dependent on daily insulin injections tocontrol blood-glucose levels. In Type-2 diabetes, on the other hand, theendocrine pancreas gradually fails to satisfy increased insulin demands,thus requiring the patient to compensate with a regime of oralmedications or insulin therapy. In the case of either Type-1 or Type-2diabetes, the failure to properly control glucose levels in the patientmay lead to complications such as heart attacks, strokes, blindness,renal failure, and even premature death.

Diabetes is a metabolic disorder where the individual's ability tosecrete insulin, and therefore to regulate glucose level, has beencompromised. For a non-diabetic person, normal glucose levels aretypically around 85-110 mg/dl, and can spike after meals to typicallyaround 140-200 mg/dl. Low glucose levels or hypoglycemia can drop belowlife-sustaining level and lead to complications such as seizures, lossof consciousness, and even death. High glucose levels or hyperglycemiaover a long period of time has been associated with far increasedchances to develop complications such as heart disease, hypertension,kidney disease, and blindness among others.

Insulin therapy is the mainstay of Type-1 diabetes management and one ofthe most widespread treatments in Type-2 diabetes, about 27% of thesufferers of which require insulin. Insulin administration is designedto imitate physiological insulin secretion by introducing two classes ofinsulin into the patient's body: Long-acting insulin, which fulfillsbasal metabolic needs; and short-acting insulin (also known asfast-acting insulin), which compensates for sharp elevations inblood-glucose levels following patient meals. Orchestrating the processof dosing these two types of insulin, in whatever form (e.g., separatelyor as premixed insulin) involves numerous considerations.

First, patients measure their blood-glucose levels (using some form of aglucose meter) on average about 3 to 4 times per day. The number of suchmeasurements and the variations therebetween complicates theinterpretation of these data, making it difficult to extrapolate trendstherefrom that may be employed to better maintain the disease. Second,the complexity of human physiology continuously imposes changes ininsulin needs for which frequent insulin dosage regimen adjustments arewarranted. Presently, these considerations are handled by a patient'sendocrinologist or other healthcare professional during clinicappointments. Unfortunately, these visits are relativelyinfrequent—occurring once every 3 to 6 months—and of short duration, sothat the physician or other healthcare professional is typically onlyable to review the very latest patient medical data. In consequence, ithas been shown that more than 60% of patients control their diabetes atsub-optimal levels, leading to unwanted complications from the disease.

Indeed, one of the major obstacles of diabetes management is the lack ofavailability of a patient's healthcare professional and the relativeinfrequency of clinic appointments. Studies have, in fact, establishedthat more frequent insulin dosage regimen adjustments, for example,every 1 to 2 weeks—improves diabetes control in most patients. Yet asthe number of diabetes sufferers continues to expand, it is expectedthat the possibility of more frequent insulin dosage regimen adjustmentsvia increased clinic visits will, in fact, decrease. And, unfortunately,conventional diabetes treatment solutions do not address this obstacle.

The device most commonly employed in diabetes management is the glucosemeter. The use of a glucose meter involves drawing a sample of bloodfrom the patient and measuring its glucose content. Such devices come ina variety of forms, although most are characterized by their ability toprovide patients near instantaneous readings of their blood-glucoselevels. This additional information can be used to better identifydynamic trends in blood-glucose levels. However, conventional glucosemeters are designed to be diagnostic tools rather than therapeutic ones.Therefore, by themselves, even state-of-the-art glucose meters do notlead to improved glycemic control.

One conventional solution to the treatment of diabetes is the insulinpump. Insulin pumps are devices that continuously infuse short actinginsulin into a patient at a predetermined rate to cover both basal needsand meals. The use of an insulin pump involves the insertion of acatheter into the patient through which insulin is infused. As withmanual insulin administration therapy, a healthcare professional setsthe pump with the patient's insulin dosage regimen during clinic visits.In addition to their considerable current expense, which prohibits theirwidespread use by patients with Type-2 diabetes, insulin pumps requirefrequent adjustment by the physician or other healthcare professional tocompensate for the needs of individual patients based upon frequentblood-glucose-level measurements.

An even more recent solution to diabetes treatment seeks to combine aninsulin pump and near-continuous glucose monitoring in an effort tocreate, in effect, an artificial pancreas regulating a patient'sblood-glucose-level with infusions of short-acting insulin. According tothis solution, real-time patient information is employed to matchinsulin dosing to the patient's dynamic insulin needs irrespective ofany underlying physician-prescribed treatment plan. While such systemsaddress present dosing requirements, they are entirely reactive and notinstantaneously effective. In consequence of these drawbacks, suchcombined systems are not always effective at controlling blood glucoselevels. For instance, such combined units cannot forecast unplannedactivities, such as exercise, that may excessively lower a patient'sblood-glucose level. And when the hypoglycemic condition is detected,the delay in the effectiveness of the insulin occasioned not only by thenature of conventional synthetic insulin but also the sub-dermaldelivery of that insulin by conventional pumps results in inefficientcorrection of the hypoglycemic event.

The most common biomarker used to access glycemic control is hemoglobinA1C (A1C for brevity). The relationship between average glucose levelsand A1C has been studied. For healthy individuals A1C is between 4.6%and 5.8%, for people with diabetes the American Diabetes Association(ADA) and the European Association for the Study of Diabetes (EASD)recommend maintaining A1C<7% that correlates to an average glucose levelbelow 150 mg/dl.

Studies have demonstrated the relationship between A1C and complication.The ADA and EASD have set the goal of getting A1C to below 7%. This waschosen as a compromise between lowering the risk for developingcomplications and the risk of severe (and potentially fatal)hypoglycemia. As a result, diabetes management has developed with itsmain goal being to bring A1C down as reflected by several consensusstatements issued by various authorities.

While the foregoing solutions are beneficial in the management andtreatment of diabetes in some patients, or at least hold the promise ofbeing so, alleviation of glucotoxicity and restoration of beta-cellfunction in patients with advanced diabetes has not been possible withexisting modalities. Thus, there continues to exist the need forsystems, devices, and/or methods that can achieve glycemic balance,alleviate glucotoxicity and/or restore beta-cell function in patientswith longstanding disease.

SUMMARY

Certain embodiments are directed to systems, devices and/or methods fortreating a patient's diabetes by providing treatment guidance. Forexample, a method for treating diabetes mellitus by alleviatingglucotoxicity and/or restoring pancreatic beta-cell function in apatient, the method comprising: storing one or more components of thepatient's insulin dosage regimen; drawing blood samples from the patientat a plurality of times; ascertaining the patient's blood glucose-levelmeasurements in said blood samples; tagging each of said bloodglucose-level measurements with an identifier reflective of when saidmeasurement was obtained; establishing the patient's current glycemicstate relative to a desired glycemic range; determining from thepatient's blood glucose-level measurements whether and by how much toadjust at least one component of the one or more components of thepatient's present insulin dosage regimen to stay within said desiredglycemic range and administer the lowest insulin dosage; adjusting saidat least one component in accordance with said determination; andperforming said steps of the method until the insulin dosage regimenrequired to stay within the desired glycemic range is lowered to apredetermined level.

In certain embodiments, the adjustment to the patient's insulin dosageregimen may be performed in substantially real time.

In certain embodiments, an initial insulin dosage regimen may beprovided by a physician or other healthcare professional.

In certain embodiments, the method may be performed without anyintervention from a doctor or other healthcare professional.

In certain embodiments, the desired glycemic range may change over timeand the adjustment to patient's insulin dosage regimen may be to getwithin the most recent desired glycemic range.

In certain embodiments, the identifiers reflective of when the readingwas obtained may be selected from Breakfast, Lunch, Dinner, Bedtime,Nighttime, and Other.

In certain embodiments, the measurements tagged as “Other” may beclassified based on the classification of the previous measurement andan elapsed time since the previous measurement.

In certain embodiments, the desired glycemic range may be defined as ablood glucose-level measurement between 85 mg/dL and 200 mg/dL.

In certain embodiments, the steps of the method may be repeated oversufficient time to achieve durable glycemic balance.

In certain embodiments, the patient's insulin dosage regimen may beadjusted to include at least one high insulin dose.

In certain embodiments, the patient's insulin dosage regime may beadjusted such that insulin is no longer administered.

Certain embodiments are directed to systems, devices and/or methods fortreating a patient's diabetes by providing treatment guidance. Forexample, a method for treating diabetes mellitus by alleviatingglucotoxicity and/or restoring pancreatic beta-cell function in apatient, the method comprising: storing one or more components of thepatient's insulin dosage regimen; drawing blood samples from the patientat a plurality of times; ascertaining the patient's blood glucose-levelmeasurements in said blood samples; tagging each of said bloodglucose-level measurements with an identifier reflective of when saidmeasurement was obtained; establishing the patient's current glycemicstate relative to a desired glycemic range; determining from thepatient's blood glucose-level measurements whether and by how much toadjust at least one component of the one or more components of thepatient's present insulin dosage regimen to stay within said desiredglycemic range and administer the lowest insulin dosage; adjusting saidat least one component in accordance with said determination in a mannerthat dampens or prevents unstable oscillations; and performing saidsteps of the method until the insulin dosage regimen required to staywithin the desired glycemic range is lowered to a predetermined level.

In certain embodiments, the unstable oscillations may be dampened orprevented by ensuring that said adjustment is limited to an intermittentvalue.

In certain embodiments, the unstable oscillations may be dampened orprevented by ensuring that the current increase in the patient's insulindosage regimen is less than the previous decrease in the patient'sinsulin dosage regimen.

In certain embodiments, the unstable oscillations may be dampened orprevented by ensuring that the current decrease in the patient's insulindosage regimen is less than the previous increase in the patient'sinsulin dosage regimen.

In certain embodiments, the unstable oscillations are dampened orprevented by inserting ‘off intervals’ between different directions ofdosage adjustments.

In certain embodiments, the determination of whether and by how much toadjust at least one component of the patient's present insulin dosageregimen may be made by calculating the total daily amount of insulin.

In certain embodiments, the determination of whether and by how much toadjust at least one component of the patient's present insulin dosageregimen may be made by calculating the amount by which any component ofthe dosage is greater than the same component for the previous dosage.

In certain embodiments, the determination of whether and by how much toadjust at least one component of the patient's present insulin dosageregimen is made by majority voting rule.

In certain embodiments, the determination of whether and by how much toadjust at least one component of the patient's present insulin dosageregimen may be subject to a consecutive dosage increment rule toestablish an off period if a more than a desired number of incrementshas occurred.

In certain embodiments, the consecutive dosage increment rule mayprevent two consecutive increments from occurring and create an offperiod after each dosage increment.

In certain embodiments, the consecutive dosage increment rule may allowmore than two consecutive increments but no more than eight consecutiveincrements.

Certain embodiments are directed to systems, devices and/or methods fortreating a patient's diabetes by providing treatment guidance. Forexample, a method for updating a patient's insulin dosage regimen toalleviate glucotoxicity and/or restore pancreatic beta-cell function,the method comprising: storing one or more components of the patient'sinsulin dosage regimen; drawing blood samples from the patient at aplurality of times; ascertaining the patient's blood glucose-levelmeasurements in said blood samples; incrementing a timer based on atleast one of the passage of a predetermined amount of time and thereceipt of each blood glucose-level measurement; tagging each of saidblood glucose-level measurements with an identifier reflective of whensaid measurement was obtained; establishing the patient's currentglycemic state relative to a desired glycemic range; determining fromthe patient's blood glucose-level measurements whether and by how muchat least one component of the one or more components of the patient'spresent insulin dosage regimen should be adjusted to stay within saiddesired glycemic range with the lowest insulin dosage; updating said atleast one component in the patient's insulin dosage regime in responseto said determination; and performing the aforementioned steps of themethod until the insulin dosage regimen required to stay within thedesired glycemic range is lowered to a predetermined level.

In certain embodiments, updating at least one of the one or morecomponents in the patient's insulin dosage regime may be done inresponse to a determination that there have been an excessive number ofblood-glucose measurements that have not been within the desiredglycemic range with the lowest insulin dosage over a predefined periodof time; and the timer may be reset.

In certain embodiments, the timer may be configured to indicate that thestep of determining from the patient's blood glucose-level measurementswhether and by how much at least one component of the one or morecomponents of the patient's present insulin dosage regimen should beadjusted should be performed after 7 days.

In certain embodiments, the timer may indicate when to perform the stepof determining from the patient's blood glucose-level measurementswhether and by how much at least one component of the one or morecomponents of the patient's present insulin dosage regimen should beadjusted, and the timer may be reset.

In certain embodiments, the desired glycemic range may change over timeand the update to patient's insulin dosage regimen may be to get withinthe most recent desired glycemic range.

In certain embodiments, the patient's insulin dosage regimen may beupdated in a manner that dampens or prevents unstable oscillations.

In certain embodiments, the scope of the oscillations may be reduced byensuring that the current increase in the patient's insulin dosageregimen is less than the previous decrease in the patient's insulindosage regimen.

In certain embodiments, the identifiers reflective of when the readingwas obtained may be selected from Breakfast, Lunch, Dinner, Bedtime,Nighttime, and Other.

In certain embodiments, the measurements tagged as “Other” may beclassified based on the classification of the previous measurement andan elapsed time since the previous measurement.

In certain embodiments, the desired glycemic range may be defined as ablood glucose-level measurement between 85 mg/dL and 200 mg/dL.

In certain embodiments, the steps of the method may be repeated oversufficient time to achieve durable glycemic balance.

In certain embodiments, the patient's insulin dosage regimen may beupdated to include at least one high insulin dose.

In certain embodiments, the patient's insulin dosage regime may beupdated such that insulin is no longer administered.

Certain embodiments are directed to systems, devices and/or methods fortreating a patient's diabetes by providing treatment guidance. Forexample, an apparatus for alleviating glucotoxicity and/or restoringpancreatic beta-cell function in a patient over time, comprising: atleast a first computer-readable memory for storing one or morecomponents in a patient's present insulin dosage regimen and thepatient's blood-glucose-level measurements determined at a plurality oftimes within a predefined period of time; at least one data input forobtaining data corresponding to the patient's blood glucose-levelmeasurements determined at the plurality of times; a timer formonitoring the predetermined time period, the timer being incrementedbased on at least one of the passage of a predetermined increment oftime and the receipt of at least one of the plurality of bloodglucose-level measurements; at least one processor operatively connectedto the at least first computer-readable memory, the processor programmedat least to: tag the plurality of blood glucose-level measurements withan identifier reflective of when the measurement was obtained;calculate, after obtaining one of the plurality of blood glucose-levelmeasurements but before obtaining a subsequent blood glucose-levelmeasurement, any deviation of the obtained blood glucose-levelmeasurement from a desired glycemic range; adjust at least one of theone or more components in the patient's insulin dosage regimen inresponse to a determination that the most recently obtained bloodglucose-level measurement was not within a desired glycemic range;wherein the timer is reinitiated after the determination that that themost recently obtained blood glucose-level measurement was not within adesired glycemic range over the predefined period of time; determine, atthe end of the predefined period of time, from a plurality of the datacorresponding to the patient's blood glucose-level measurements, thevariation of at least one of the one or more components in the patient'sinsulin dosage regimen that is necessary in order to maintain thepatient's blood glucose-level measurements within a predefined range;reinitiate said timer after said adjustment and said determination; andrepeatedly tag said measurements, calculate said deviation, adjust saidat least one component, and determine said variation until the insulindosage regimen required to stay within the desired glycemic range islowered to a predetermined level.

In certain embodiments, a data entry device may enable a user to modifythe identifier associated with each blood-glucose-level measurementdata-input.

In certain embodiments, the at least one processor may further determineon a predefined schedule whether and by how much to vary at least one ofthe one or more components in the patient's present insulin dosageregimen.

In certain embodiments, the at least one processor may furtherdetermine, at the end of the predetermined period of time, from theplurality of the data corresponding to the patient's blood-glucose-levelmeasurements, if the patient's blood-glucose level measurements fallwithin or outside of said predefined range, and vary at least one of theone or more components in the patient's present insulin dosage regimenonly if the patient's blood-glucose level measurements fall outside ofsaid predefined range.

In certain embodiments, the at least one processor may further determinefrom the patient's blood-glucose-level measurements whether thepatient's blood-glucose-level measurements represent a normaldistribution.

In certain embodiments, the determination of whether the patient'sblood-glucose-level measurements represent a normal distribution maycomprise determining whether the third moment of the distribution of thepatient's blood-glucose-level measurements fall within said predefinedrange.

In certain embodiments, the one or more components in the patient'spresent insulin dosage regimen may comprise a long-acting insulin dosagecomponent.

In certain embodiments, the one or more components in the patient'spresent insulin dosage regimen may comprise a short-acting insulindosage component defined by a carbohydrate ratio or a fixed meal dose,and plasma glucose correction factor.

Certain embodiments are directed to systems, devices and/or methods fortreating a patient's diabetes by providing treatment guidance. Forexample, a system for alleviating glucotoxicity and/or restoringpancreatic beta-cell function in a patient, the system comprising: atleast one memory for storing data corresponding at least to one or morecomponents of a patient's present insulin dosage regimen, and datacorresponding at least to the patient's blood-glucose-level measurementsmeasured at a plurality of times during a predetermined time period; andat least one processor operatively connected to the at least one memory,the at least one processor configured to: initiate a timer to monitorthe predetermined time period; increment the timer based on at least oneof the passage of a predetermined increment of time and the receipt ofat least one of the plurality of blood glucose-level measurements; tagthe plurality of blood glucose-level measurements with an identifierreflective of when the measurement was obtained; calculate, afterobtaining one of the plurality of blood glucose-level measurements butbefore obtaining a subsequent blood glucose-level measurement, anydeviation of the obtained blood glucose-level measurement from a desiredglycemic range; adjust at least one of the one or more components in thepatient's insulin dosage regimen in response to the determination thatthe most recently obtained blood glucose-level measurement was notwithin a desired glycemic range; wherein the timer is reinitiated afterthe determination that was not within a desired glycemic range; anddetermine, at the end of the predetermined time period, from a pluralityof the data corresponding to the patient's blood glucose-levelmeasurements whether and by how much to vary at least one of the one ormore components in the patient's present insulin dosage regimen in orderto maintain the patient's blood glucose-level measurements within apredefined range; wherein the timer is reinitiated after thedetermination of whether and by how much to vary at least one of the oneor more components in the patient's present insulin dosage regimen; andrepeatedly increment said timer, tag said measurements, calculate saiddeviation, adjust said at least one component, and determine saidvariation until the insulin dosage regimen required to stay within thedesired glycemic range is lowered to a predetermined level.

In certain embodiments, the at least one memory and at least oneprocessor may be resident in a single apparatus.

In certain embodiments, the single apparatus may further comprise aglucose meter.

In certain embodiments, the system may further comprise a glucose meterthat is separate from the single apparatus, the glucose meter may beadapted to communicate to the at least one memory of the singleapparatus the data corresponding at least to the patient'sblood-glucose-level measurements determined at a plurality of times.

In certain embodiments, the single apparatus may further comprise dataentry means for entering data inputs corresponding at least to thepatient's blood-glucose-level measurements determined at a plurality oftimes directly into the at least one memory.

In certain embodiments, the system may further comprising data entrymeans disposed at a location remote from the single apparatus forremotely entering data corresponding at least to the one or morecomponents in the patient's present insulin dosage regimen into the atleast one memory.

In certain embodiments, the system may further comprise at least firstdata entry means disposed at a location remote from the at least onememory and at least one processor for remotely entering data inputscorresponding at least to the one or more components in the patient'spresent insulin dosage regimen into the at least one memory, and atleast second data entry means, disposed at a location remote from the atleast one memory, at least one processor and at least first data entrymeans, for remotely entering data inputs corresponding at least to thepatient's blood-glucose-level measurements determined at a plurality oftimes into the at least one memory.

In certain embodiments, the data corresponding at least to the patient'sblood-glucose-level measurements determined at a plurality of times maybe associated with an identifier indicative of when the measurement wasinput into the memory.

In certain embodiments, the system may further comprise data entry meansenabling a user to define the identifier associated with theblood-glucose-level measurement data.

In certain embodiments, the system may further comprise data entry meansenabling a user to confirm the correctness of the identifier associatedwith the blood-glucose-level measurement data.

In certain embodiments, the system may further comprise data entry meansenabling a user to modify the identifier associated with eachblood-glucose-level measurement data.

In certain embodiments, the processor may be programmed to determine ona predefined schedule whether and by how much to vary at least one ofthe one or more components in the patient's present insulin dosageregimen.

In certain embodiments, the processor may be programmed to determinefrom the data inputs corresponding at least to the patient'sblood-glucose-level measurements determined at a plurality of times ifthe patient's blood-glucose level measurements fall within or outsidesaid desired glycemic range, and to vary at least one of the one or morecomponents in the patient's present insulin dosage regimen only if thepatient's blood-glucose level measurements fall outside of said desiredglycemic range.

In certain embodiments, the processor may be further programmed todetermine from the data inputs corresponding at least to the patient'sblood-glucose-level measurements determined at a plurality of timeswhether the patient's blood-glucose-level measurements determined at aplurality of times represent a normal or abnormal distribution.

In certain embodiments, the determination of whether the patient'sblood-glucose-level measurements determined at a plurality of timesrepresent a normal or abnormal distribution may comprise determiningwhether the third moment of the distribution of the patient'sblood-glucose-level measurements determined at a plurality of times fallwithin said desired glycemic range.

In certain embodiments, the one or more components in the patient'spresent insulin dosage regimen may comprise a long-acting insulin dosagecomponent, and the processor may be programmed to determine from theidentifier indicative of when a measurement was input into the memory atleast whether the measurement is a morning or bed-timeblood-glucose-level measurement, to determine whether the patient'smorning and bed-time blood-glucose-level measurements fall within apredefined range, and to determine by how much to vary the patient'slong-acting insulin dosage component only when the patient's morning andbed-time blood-glucose-level measurements are determined to fall outsideof said desired glycemic range.

In certain embodiments, in connection with the determination of by howmuch to vary at least one of the one or more components in the patient'spresent insulin dosage regimen, the at least one processor may beprogrammed to factor in an insulin sensitivity correction factor thatdefines both the percentage by which any of the one or more componentsof the insulin dosage regimen may be varied and the direction in whichany fractional variations in any of the one or more components arerounded to the nearest whole number.

In certain embodiments, the at least one memory may further store datacorresponding to a patient's present weight, and the insulin sensitivitycorrection factor may be in part determined from the patient's presentweight.

In certain embodiments, the determination of by how much to vary thelong-acting insulin dosage component of a patient's present insulindosage regimen may be a function of the present long-acting insulindosage, the insulin sensitivity correction factor, and the patient'sblood-glucose-level measurements.

In certain embodiments, the one or more components in the patient'spresent insulin dosage regimen may comprise a short-acting insulindosage component defined by a carbohydrate ratio and plasma glucosecorrection factor, and the processor may be programmed to determinewhether and by how much to vary the patient's carbohydrate ratio andplasma glucose correction factor.

In certain embodiments, in connection with the determination of by howmuch to vary at least one of the one or more components in the patient'spresent insulin dosage regimen, the at least one processor may beprogrammed to factor in an insulin sensitivity correction factor thatdefines both the percentage by which any one or more components of theinsulin dosage regimen may be varied and the direction in which anyfractional variations in the one or more components are rounded to thenearest whole number.

In certain embodiments, the determination of by how much to vary thepresent plasma glucose correction factor component of a patient'sinsulin dosage regimen may be a function of a predefined value dividedby the mean of the total daily dosage of insulin administered to thepatient, the patient's present plasma glucose correction factor, and theinsulin sensitivity correction factor.

In certain embodiments, a value representing twice the patient's dailydosage of long-acting insulin in the present insulin dosage regimen maybe substituted for the mean of the total daily dosage of insulinadministered to the patient as an approximation thereof.

In certain embodiments, the plasma glucose correction factor componentof the patient's insulin dosage regimen may be quantized to predefinedsteps of mg/dL.

In certain embodiments, the determination of by how much to vary thepresent carbohydrate ratio component of a patient's insulin dosageregimen may be a function of a predefined value divided by the mean ofthe total daily dosage of insulin administered to the patient, thepatient's present carbohydrate ratio, and the insulin sensitivitycorrection factor.

In certain embodiments, a value representing twice the patient's dailydosage of long-acting insulin in the present insulin dosage regimen maybe substituted for the mean of the total daily dosage of insulinadministered to the patient as an approximation thereof.

In certain embodiments, the at least one processor may be programmed todetermine a correction factor that allows variations to the carbohydrateratio component of a patient's insulin dosage regimen to be altered inorder to compensate for a patient's individual response to insulin atdifferent times of the day.

In certain embodiments, the one or more components in the patient'spresent insulin dosage regimen may comprise a long-acting insulin dosagecomponent, and the determination of by how much to vary the long-actinginsulin dosage component may be constrained to an amount of variationwithin predefined limits.

In certain embodiments, the one or more components in the patient'spresent insulin dosage regimen may comprise a short-acting insulindosage component defined by a carbohydrate ratio and plasma glucosecorrection factor, and the determination of by how much to vary any oneor more of each component in the short-acting insulin dosage may beconstrained to an amount of variation within predefined limits.

In certain embodiments, the one or more components in the patient'spresent insulin dosage regimen may comprise a short-acting insulindosage component taken according to a sliding scale, and the processormay be programmed to determine whether and by how much to vary at leastthe sliding scale in order to maintain the patient's futureblood-glucose-level measurements within a predefined range.

In certain embodiments, the determination of by how much to vary thesliding scale may be constrained to an amount of variation withinpredefined limits.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings and figures facilitate an understanding of thevarious embodiments of this technology.

FIG. 1 is a simplified schematic of an apparatus according to certainexemplary embodiments.

FIG. 2 is a drawing of a representative display for providinginformation to a patient.

FIG. 3 is a drawing of another representative display for providinginformation to a patient.

FIG. 4 is a drawing yet another representative display for providinginformation to a patient.

FIG. 5 is a drawing of still another representative display forproviding information to a patient.

FIG. 6 is a simplified diagram of an apparatus for employing thedisclosed system, according to certain embodiments thereof.

FIG. 7 is a simplified diagram of an apparatus for employing thedisclosed system, according to certain embodiments.

FIG. 8 is a simplified diagram of an apparatus for employing thedisclosed system, according to certain embodiments thereof.

FIG. 9 is a schematic view of an exemplary arrangement, according tocertain embodiments.

FIG. 10 is a schematic view of an exemplary arrangement for employing,according to certain embodiments.

FIG. 11 is a generalized diagram of the steps employed in updating apatient's insulin dosage regimen according to certain exemplaryembodiments.

FIG. 12 is a flowchart of an exemplary algorithm employed in updating apatient's insulin dosage regimen according to certain exemplaryembodiments

FIG. 13 illustrates a subject with low variability of glucose levels.

FIG. 14 illustrates a subject with high variability of glucose level.

FIG. 15 illustrates a patient with varying level of glycemicvariability.

FIG. 16 illustrates a subject with a high glucose level and lowvariability.

FIG. 17 illustrated insulin dosage of a subject with a high glucoselevel and low variability.

FIG. 18 illustrates a subject with low glucose with high variability.

FIG. 19 illustrates insulin dosage of a subject with low glucose withhigh variability.

FIG. 20 illustrates a subject with low glucose with low variability.

FIG. 21 illustrates insulin dosage of a subject with low glucose withlow variability.

FIG. 22 illustrates a subject with high glucose with high variability.

FIG. 23 illustrates insulin dosage of a subject with high glucose withhigh variability.

FIG. 24 illustrates blood glucose levels of a subject on a premixedinsulin therapy.

FIG. 25 illustrates insulin dosage of a subject on premixed insulintherapy.

FIG. 26 illustrates blood glucose levels of a subject on a premixedinsulin therapy.

FIG. 27 illustrates insulin dosage of a subject on premixed insulintherapy.

FIG. 28 illustrates blood glucose levels of a subject taking arelatively small daily total of ˜45 units per day almost equally dividedbetween basal and bolus.

FIG. 29 illustrates insulin dosage of a subject taking a relativelysmall daily total of ˜45 units per day almost equally divided betweenbasal and bolus.

FIG. 30 illustrates the weekly mean glucose (and regression line),cumulatively for all patients in this example, according to certainembodiments.

FIG. 31 illustrates the weekly mean glucose (and regression line),cumulatively in groups I and II in this example, according to certainembodiments.

FIG. 32 illustrates the weekly mean glucose in group III (due to lesserdata points, a regression line was not plotted) in this example,according to certain embodiments.

FIG. 33 illustrates the weekly mean glucose (and regression line) ofpatients with and without frequent hypoglycemia. During the active 12weeks weekly mean glucose improved when possible in this example,according to certain embodiments.

FIG. 34 illustrates the distribution of hypoglycemic glucose readingsduring the 12-week active phase and the 4-week run-in period in thisexample, according to certain embodiments.

FIG. 35 illustrates the frequency of minor hypoglycemia (glucose<65mg/dl) during each quartile for patients with or without frequenthypoglycemia (>85 events per patient-year) in this example, according tocertain embodiments.

FIG. 36 illustrates the total daily insulin in patients with differentfrequencies of minor hypoglycemia. During the active 12-week period, thefrequency and severity of hypoglycemia decreased in this example,according to certain embodiments.

FIG. 37 illustrates the insulin dosage and average blood glucose levelof a subject on insulin therapy over a nine-month period.

FIG. 38 illustrates the insulin dosage and average blood glucose levelof a subject on insulin therapy over a fifteen-month period.

FIG. 39 illustrates the insulin dosage and average blood glucose levelof a subject on insulin therapy over a ten-month period.

DETAILED DESCRIPTION

The following description is provided in relation to several embodimentswhich may share common characteristics and features. It is to beunderstood that one or more features of any one embodiment may becombinable with one or more features of the other embodiments. Inaddition, any single feature or combination of features in any of theembodiments may constitute additional embodiments.

In this specification, the word “comprising” is to be understood in its“open” sense, that is, in the sense of “including”, and thus not limitedto its “closed” sense, that is the sense of “consisting only of”. Acorresponding meaning is to be attributed to the corresponding words“comprise”, “comprised” and “comprises” where they appear.

The subject headings used in the detailed description are included onlyfor the ease of reference of the reader and should not be used to limitthe subject matter found throughout the disclosure or the claims. Thesubject headings should not be used in construing the scope of theclaims or the claim limitations.

The term “insulin dosage function” or “IDF” as used herein with respectto certain embodiments refers to a lookup table indicative of an insulinregimen, a protocol, or a combination thereof that a user follows. Forexample, for a patient following premixed insulin regimen the insulindosage function may contain two numbers associated with two eventsreflective of two insulin injection per day, say X insulin units withbreakfast and Y insulin units with dinner. The term IDF history as usedherein with respect to certain embodiments refers to chronology ofinsulin dosage functions and external insulin dosage functions viewed asone data set. The first IDF in an IDF history is the active insulindosage function or the lookup table currently used to recommend the useran appropriate insulin dose per a particular event and event relatedinformation. The next record is the second IDF in IDF history thefollowing is the third IDF in IDF history and so forth through theexisting records in IDF history

The term “partial update” as used herein with respect to certainembodiments refers to the operation of updating a single dosagecomponent in an insulin dosage function. In certain embodiments, apartial update may change more than one dosage components. In certainembodiments, a partial update may not interfere with the synchronousdosage adjustment frequency. In certain embodiments, an event thatcaused a partial updated may be excluded when the time to perform asynchronous adjustment is due.

The term “full update” as used herein with respect to certainembodiments refers to the operation of assessing insulin dosagecomponents to determine if and by how much to change one or more of thedosage components. In certain embodiments, the operation of a fullupdate results in a reset of the synchronous clock. In certainembodiments, the operation of a full update may result in data expiringfrom the period under evaluation. For example, certain embodiments mayemploy a counter to determine the number of hypoglycemic events thatoccurred within a given interval, the process of a full update may causea reset of that counter.

The terms “severe hypoglycemic event” or “SHE” as used herein withrespect to certain embodiments refers to blood glucose value below acertain threshold. In certain embodiments, a severe hypoglycemic eventis a patient history event with glucose data less than 55 mg/dl. Incertain embodiments, a severe hypoglycemic event is a patient historyevent with glucose data less than 40, 45, 50, 55, 60, 65, 70 mg/dl orcombinations thereof.

Certain embodiments are directed to a therapeutic device which is aglucose meter equipped with artificial intelligence (AI) and capable ofoptimizing medication dosage of patients treated with various types ofinsulin, including optimizing combination of insulin types, i.e., bothshort and long acting insulin. Certain embodiments monitor patientglucose reading and additional parameters and modify insulin dosage asneeded in a similar manner to what an endocrinologist, or otherqualified health care provider, would do if that person had continuousaccess to patient's data. By dynamically modifying medication dosagebased on individual lifestyle and changing needs an optimal dosage levelis reached. In turn, this leads to superior glycemic control and betterpatient prognosis.

Glycemic Balance

A goal of diabetes management may be achieving glycemic balance and/orimproved glycemic composite index (GCI) weighing both A1C andhypoglycemia. Another goal of diabetes management may be moving apatient towards glycemic balance and/or improved GCI weighing at leastA1C and hypoglycemia. Another goal of diabetes management may bealleviating glucotoxicity and/or restoring pancreatic beta-cell functionthrough the achievement of glycemic balance and/or improved GCI. Thereare several potential ways of minimizing a combination of two parameterssometimes referred to as minimizing a cost function of two arguments.The definition of the cost function is significant by itself as itsshape may determine what type of solution for the minimization problemexists. Convex cost function can be minimized by several methods and theminimal solution is unique. Optimization of convex function is wellstudied and books like “Convex Optimization” by Boyd and Vandenberghe(Cambridge University Press, 2004; ISBN-10: 0521833787) and othersdescribes several known methods to perform such optimization.Unfortunately, widely acceptable definitions of what is “an acceptablelevel of hypoglycemia” do not exist. However, certain aspects of thepresent disclosure are aimed at setting the goal of achieving a betterglycemic balance or an improved GCI by minimizing one argument whilemaking an effort to keep the other argument under a certain threshold.Certain aspects of the present disclosure are aimed at setting the goalof guiding a subject to a glycemic balance or an improved GCI byminimizing one argument while making an effort to keep the otherargument under a certain threshold. For example, one possible approachis to minimize glycemic index (GI) as long as it is above a certainthreshold while keeping the frequency of hypoglycemia below anotherthreshold; wherein GI is a measure of a set reducing a plurality ofhistoric blood glucose level to a single variable. For example GI can bethe mean or median of a given set of glucose values. Other metrics canalso be combined like the minimum, maximum, minimum of the mean ormedian, and other combinations like pattern recognition capable ofreducing a multidimensional data set to a single value. In certainembodiments, it may be desired to increase one or more of the insulindosage component in order to reduce glycemic index assuming glycemicindex is above 120 mg/dl and provided that there have been no more than3 low blood glucose values during the period under observation. Othernumbers are also contemplated like increasing one or more of the insulindosage components if glycemic index is above 150, 140, 130, 110, 100,90, or 80 mg/dl and provided that there were no more than 1, 2, 4, 5, 6,7, 8, 9, or 10 low blood glucose values during the period underobservation. In certain embodiments low blood glucose values may bedefine as glucose level below 80, 75, 70, 65, 60, 55, 50, or 45 mg/dl.Other methods as disclosed herein may also be chosen.

In certain embodiments, as typically done in constrained optimization adual approach is to reduce the frequency of hypoglycemia as long as GIis below a certain threshold. For example, in certain embodiments, itmay be desired to reduce one or more of the insulin dosage components,if the frequency of hypoglycemia is more than 3 during the observedinterval and provided that GI is less than 200 mg/dl. Other values like1, 2, 4, 5, 6, 7, or 8 hypoglycemic episodes may be combined withglycemic a index less than 250, 240, 230, 220, 210, 190, 180, 170, 160,150, 140, 130, 120 can also be used.

For example, to improve GCI certain embodiments may chose to reduce meanglucose as long as the rate of hypoglycemia does not exceed a certainthreshold. An exemplary algorithm that achieve that can be described asfollows:

count the number of hypoglycemic episodes over a given time to determinehypoglycemia rate (HR)

-   if HR>N-   reduce insulin level

otherwise

-   -   calculate glycemic index (GI)    -   if GI<A₁        -   decrease insulin dosage    -   if GI>A₂    -   increase insulin dosage.

The unacceptable hypoglycemic rate threshold (N) may be set to 80events/year, although other numbers such as 50, 60, 70, 75, 85, 90, 100,110 or 120 events/year may also be used. The two other thresholds A₁ andA₂ may be selected to drive GI to a desired target. For example one canset a lower level of 80 mg/dl and a higher level of 130 mg/dl, althoughother combinations of numbers may also be used. For example, glucosevalues of 60, 65, 70, 75, 85, 90, 95, 100, 105 or 110 mg/dl can be usedas the lower threshold A₁, and glucose values of 110, 115, 120, 125,135, 140, 145, 150, 155 or 160 mg/dl can be used as the upper thresholdA₂.

The GI is based on various statistics that may be derived from availableglucose data. For example, statistics that may be used are mean, median,min, max, other mathematical operator that can be extract from aparticular set of glucose data (e.g. pattern detection), or combinationsthereof.

Alleviating Glucotoxicity and Restoring of Beta-Cell Function

Type 2 diabetes is increasing worldwide with a trend of declining age ofonset. It may be characterized by insulin resistance, a physiologicalcondition in which insulin-sensitive tissues in the body (i.e., muscle,fat, and liver) fail to respond to the normal actions of the hormoneinsulin or at least have a reduced responsiveness to the normal actionsof insulin. Insulin resistance in muscle and fat cells may reduceglucose uptake, while insulin resistance in liver cells may reducesuppression of glucose production and/or release, resulting in increasedlevels of insulin and glucose in the bloodstream. If insulin resistanceexists, more insulin may need to be secreted by the pancreas.

The ability to secrete adequate amounts of insulin may be determined bythe functional integrity of beta cells and their overall mass. Whileinsulin resistance and mild elevation of ambient glucose (i.e., impairedfasting glucose and impaired glucose tolerance) can last for decades,excessive hyperglycemia typically develops over a period of a few years.Hyperglycemia may be toxic to a variety of tissues, including pancreaticbeta cells. Injured beta cells may secrete less insulin, may give riseto more severe hyperglycemia. Once full-blown hyperglycemia develops, itmay impose further metabolic injury on pancreatic beta cells, which maycause further insulin deficiency and/or additional hyperglycemia, andmay ultimately result in beta-cell demise. The detrimental effect ofexcessive glucose concentrations may be referred to as “glucotoxicity.”This pathophysiological vicious circle may be one of the main causes forthe progressive nature of diabetes.

Long-term preservation of beta-cell function may improve long-termglycemic control, improve the response to medications (including insulintherapy), reduce the frequency of treatment-related hypoglycemia, and/ordiminish complications. Therefore, recovery of beta-cell function wouldbe clinically useful. Simply stated, patients with higher beta-cellfunction may have superior outcomes, while the less endogenous insulinthe patients secretes (even while treated with insulin) the less likelythe patient will achieve therapy goals and glycemic stability.

Alleviation (or at least reduction) of glucotoxicity and recovery ofbeta-cell function has been demonstrated in newly-diagnosed diabetes.For example, one study reported that intensive insulin therapy oftwo-week duration administered to a patient with new-onset diabetes andacute ketoacidosis reduced blood glucose levels, to the point that afteran additional four weeks it induced frequent hypoglycemia. The insulindose was rapidly reduced, and insulin was discontinued two weeks later.At three-month follow up, the patient's A1C value was 6.8%. Thus, it wasreported that this short-term intensive insulin regime alleviatedglucotoxicity and restored beta-cell function. (J. Mayfield, et al.,Insulin Therapy for Type 2 Diabetes: Rescue, Augmentation, andReplacement of Beta-Cell Function, American Family Physician70:3:E489-501 (Aug. 1, 2004.) However, alleviation of glucotoxicity andrestoration of beta-cell function has not previously been demonstratedin patients over longer periods of time, or in patients with advanceddiabetes.

Clinical data show that maintaining glycemic balance in accordance withthe disclosed systems, devices, and methods over time can alleviateglucotoxicity and restore pancreatic beta-cell function. The term“durable glycemic balance” as used herein with respect to certainembodiments refers to achievement of glycemic balance and/or improvedGCI as disclosed herein for a period of time longer than about threemonths.

As is known in the art, the standard of care for insulin therapy isgenerally not to exceed 250 insulin units per day. The conventionalrationale for not exceeding that dosage is that it risks causingdangerous hypoglycemia. The term “high insulin dose” as used herein withrespect to certain embodiments refers to insulin dosage of more thanabout 250 IU per day for one or more days.

Through the use of the systems, devices, and methods disclosed herein,including the use thereof by healthcare professionals trained inaccordance with the systems, devices, and methods disclosed herein, ithas been discovered that achieving durable glycemic balance canalleviate (or at least reduce) glucotoxicity and restore (or partiallyrestore) beta-cell function, as evidenced clinically by decreased thedosing of insulin required to maintain glucose levels.

For example, as shown in FIG. 37 , an exemplary system was used in apatient over a period of 9 months. This process initially led to asignificant increase in insulin dosage. The changes in insulin dosage,from 240 units/day to over 400 units/day over the initial 4 monthperiod, moved the subject's blood glucose level into the normal rangeover the subsequent 5 month period. The patient's A1C was 12.5% atbaseline and 6.6% after 4 months of use. During the following 5 months,the blood glucose level was maintained at the near-normal range whilereducing insulin dosage back to about 240 units/day. With higherresidual beta-cell function the patient required less insulin to achievethe same therapeutic outcome, demonstrating alleviation of glucotoxicityand restoration of beta-cell function.

As shown in FIG. 38 , an exemplary the system was used in anotherpatient over a period of 15 months. The patient's glycemic profileshowed a nearly 3-fold increase in insulin over a full year (from 150units in April 2013 to 400 units in April 2014), while the blood glucoselevel decreased to an optimal range within 4 months (from 425 mg/dl inApril 2013 to 180 mg/dl in August 2013). Maintaining the blood glucoselevel in an optimal range eventually resulted in a gradual decrease inthe amount of insulin required. Over a later three-month period (fromApril 2014 to June 2014) the insulin dose required to maintain glycemicbalance nearly halved (from 400 IU/day to about 200 IU/day). With higherresidual beta-cell function the patient required less insulin to achievethe same therapeutic outcome, demonstrating alleviation of glucotoxicityand restoration of beta-cell function.

As shown in FIG. 39 , an exemplary the system was used in anotherpatient over a period of 10 months. The patient's glycemic profileshowed a decrease in insulin over a the first 4 months (from 415units/day in March 2013 to 240 units/day in July 2013), while the bloodglucose level remained in an optimal range. Maintaining the bloodglucose level in an optimal range resulted in a decrease in the amountof insulin required. In addition, over the same time period, thefrequency of hypoglycemic events (e.g., glucose measurements below 4mmol/L) was reduced. Over the subsequent 6 months (from July 2013 toDecember 2013) the insulin dose required to maintain glycemic balanceremained nearly constant at 240-270 units/day (reduced significantlyfrom the earlier dosage of 415 units/day). With higher residualbeta-cell function the patient required less insulin to achieve the sametherapeutic outcome, demonstrating alleviation of glucotoxicity andrestoration of beta-cell function.

These cases illustrate that the use of the systems, devices, and methodsas disclosed herein provides a protective facet to diabetes treatment bypersistently assessing insulin requirements and increasing or reducingdosage as needed. This facet is particularly important in patients whopartially restore endogenous insulin secretion because over time theyrequire significantly less exogenous insulin to maintain glycemicbalance and to prevent dangerous hyperglycemia.

Hypoglycemic Events

To correctly account for hypoglycemic events for the purpose of insulinadjustment it is useful that such events would be appropriately tagged.In general, a non-fasting glucose level is reflective of the previousinsulin injection. For example, a lunch glucose reading is reflective ofthe effect that the breakfast insulin bolus may had on the user bloodglucose levels. In some cases, as a non-limiting example, when a userfeels symptoms of hypoglycemia, they may measure glucose outside oftheir regular schedule. Such glucose data point is typically marked as‘Other’. If the ‘Other’ glucose level is low it is useful to identifythe insulin injection that most likely caused the low ‘Other’ bloodglucose level so that the appropriate insulin dosage component would bereduced accordingly. The process of reclassifying an ‘Other’ eventrelies on the timestamp of the ‘Other’ event and the time that past froma previous event that was not classified as ‘Other’, for example a mealevent. The pharmacokinetic profile of the particular insulin used by theuser can help set a time window during which an injection may had aparticular effect that resulted in a low blood glucose level. In oneexample, the user may be administering fast-acting insulin which may beactive 30 minutes post injection and its effect will completely wear offwithin 6 hours post injection. For this example, if a ‘Lunch’ event isrecorded at 12 PM and a low blood glucose level is recorded as ‘Other’at 12:10 PM it is unlikely that this low blood glucose level is a resultof the Lunch fast-acting insulin injection because it happened tooquickly and fast-acting insulin takes longer to start affecting bloodglucose levels. Similarly, if an ‘Other’ event is recorded after 6 PM itis unlikely the cause of the lunch fast-acting injection since itseffect has already worn off the user. Therefore, it is understood thatfor a fast acting insulin, with a pharmacokinetic activity profile of 30minutes to 6 hours from an injection, low blood glucose levels, taggedas ‘Other’, that occurs within 30 minutes to 6 hours from an injectionevent are likely a result of that injection. Accordingly, it may bedesired to reduce the dosage component that most likely caused that lowblood glucose level.

Other types of insulin may also be considered. Some rapid-acting orultra rapid-acting insulin may have a pharmacokinetic activity profilewhere they start affecting blood glucose levels within 15 minutes fromadministration and their effect wears off within 3 hours. Older types ofinsulin, like Regular Insulin (e.g. by Eli Lilly), may take 45 minutesto start affecting blood glucose levels and as many as 8 hours to wearoff. If a user administers pre-mixed or biphasic insulin then thepharmacokinetic profile of such drugs, e.g. Humulin 70/30, Novolin70/30, Humalog Mix 75/25, or Novolog Mix 70/30, may be affecting bloodglucose levels starting about 1 hour after injection and ending around12 hours post injection. It is also appreciated that long-actinginsulin's, such as Lantus® or Levemir®, have a fairly flatpharmacokinetic profile and their activity level is nearly constant overa 24 hours period. Therefore, if a user is administering a combinationof long acting and fast acting insulin for their diabetes managementglucose levels tagged as ‘Other’ that falls outside of a particular timewindow following a fast-acting insulin administration are most likelyattributed to the background long-acting insulin injection. Accordingly,if an ‘Other’ event recorded a low glucose level and appeared 7 hourspost the last meal event recorded in history, that glucose level can beused to reduce the long-acting insulin dosage component.

In certain embodiments, it may be desired to reduce an insulin dosagecomponent as soon as a very low blood glucose (VLG) level has beenlogged into history. A VLG level may be define as blood glucose levelbelow 60 mg/dl, although other numbers, such as below 70, 65, 55, 50,45, 40 or 35 mg/dl as well as other numbers in similar ranges can alsobe used. Once a VLG has been logged it is desired that the dosagecomponent that has most likely caused that VLG will be reduced. Thatdosage component can be proportionally reduced by 10%, 20%, 30%, 40% or50%, or reduced by a fixed number of insulin units such as reduced by 1,2, 3, 4, 6, 8, 10, or other reasonable numbers in that range. Thereduction of dosage may also be a combination of the two, for exampledosage component will be reduced by the greater of (X units or Y %). Inthis case, if a user is administering 10 insulin units say X=2 and Y=10%than the greater of 2 [insulin units] or 10% of 10 [insulin units] is 2[insulin units], in which case the new dosage component will be 8insulin units, instead of the previous component that was 10. In othercases the combination may be the smaller of (X units or Y %). If thelatter was applied to the previous example than the smaller of the twois 1 insulin units and the new recommended dosage component would be 9insulin units, instead of the previous component that was 10. Anotheralternative to a dosage component reduction is a ‘roll back’, that isfind the previous dosage component that is lower than the current oneand replace the current component with the previous one. For example, ifan insulin dosage component was 10 units and was later increased to 13units. And, a while later the dosage component of 13 is suspected as thereason behind a VLG it may be desired to replace the component 13 withthe previous lower value of 10. This is done regardless ofproportionality or a fixed minimal/maximal reduction because accordingto the data in the device history the previous value of 10 did not causeany VLG.

In some cases it would be appreciated that glucose values may be low butnot very low. A range for low glucose LG can be defined as values thatare not VLG, contemplated before, yet lower than a particular value like75 mg/dl. Other numbers can also be used for the upper threshold like,90, 85, 80, 70, 65, 60, 55, 50, 45 or 40 mg/dl or other reasonablenumbers in that range. In some embodiments, it may be desired to accountfor a plurality of LG values even if independently none of them accountsas a VLG to updated insulin dosage components. This is particularlyuseful if a similar event is suspicious as causing the LG values. Forexample if a dosage was installed on Monday evening and Tuesday lunch LGvalue is recorded and Wednesday lunch LG value is recorded it may bedesired to reduce the breakfast dosage component. Another example can bethat 3 LG values have been recorded for different events within a 24hours period. Yet another example can be that 4 LG values have beenrecorded for different events from the time last dosage was instated.Other combinations, like 3 or more LG values for a particular event, 2LG values or more within a 24 hours period, or 2, 3, 4, 5, 6, 7, 8, 9,10 LG values recorded from the time stamp when current dosage wasinstalled, are also contemplated.

It is appreciated that low blood glucose values are typically anindication the user is administering too much insulin for its currentmetabolic state. This condition may lead to VLG or to a severehypoglycemic event that is potentially life threatening. It is thereforedesired that a system adjusting insulin dosage is capable of reducingone or more of the user's insulin dosage components in an attempt toprevent the situation from having a negative clinical outcomes. Theaforementioned behavior of a system that adjusts insulin dosage on asynchronous basis, e.g., once a week, can be summarized as follows: ifone or more VLG values have been logged by the system an effort is madeto identify a dosage component that may have caused that one or more VLGvalues and reduced it according to dosage reduction rules. This responseto an occurrence of one or more VLG values may or may not reset thesynchronous time base for the insulin adjustment process. If one or moreLG values have been logged by the system there is an attempt to assessthe cause of the one or more LG values and to respond accordingly byreducing one or more of the insulin dosage components. Such a reductionmay or may not lead to a reset of the synchronous clock.

Insulin Adjustment

When adjusting insulin dosage it may be desirable to preventoscillations, i.e., low blood glucose levels leading to a dosagereduction leading to higher blood glucose levels leading to a dosageincrease leading to lower blood glucose levels and so forth. Severalmechanisms can be used to dampen, reduce, or substantially decreaseunstable dosage oscillations. One such mechanism would be inserting ‘offintervals’ between different directions of dosage adjustments. Forexample a system may follow a rule that if a dosage was reduced frombaseline then the next dosage adjustment step can be further reductionor keep in place but not an increase. This way a dosage increase willtypically never follow a dosage decrease reducing the likelihood ofblood glucose levels oscillations. Another mechanism can be that if adosage reduction occurred and an increase is recommended in thefollowing dosage adjustment step, then such increase should typically belimited to an intermittent value. The increase can be limited to be lessthan a particular level, for example, less than the value that was usedbefore the reduction occurred, or no greater than the level that wasused before the reduction occurred, or not to exceed that level that wasused before the reduction occurred by more than 5%, 10%, 15% or 20%, or2 insulin units or 4 insulin units, or other similar expression. Thisway it is understood that the insulin dosage adjustment system is usingshort term memory by not only reviewing the blood glucose dataaccumulated in history during the period under review but also utilizingthe dosage history that preceded the period under review.

It would be appreciated that a similar mechanism can be used for theother direction, i.e., a dosage decrease that followed a prior increase.Such decrease may also be limited, dampened, reduced, to prevent, orsubstantially prevent unstable oscillations. However, it is understoodthat insulin dosage reduction is typically done to improve the safety ofthe therapy and prevent future hypoglycemia.

In some embodiments it is desired to prevent consecutive dosageincrements as the user response to such increments may be delayed. Adelayed response to insulin dosage increments may result in severehypoglycemia or other adverse events. In some embodiments thecontemplated system may use a consecutive dosage increment rule toestablish an ‘off period’ if an excessive number of consecutive dosageincrements occurred. For example, a system may employ a rule thatprevents 2 consecutive increments from occurring, i.e., creating an ‘offperiod’ after each dosage increment allowing for a delayed response.Other rules may also apply, for example, allowing for two consecutiveincrements in dosage but not 3, or allowing for 3 consecutive dosageincrements but not 4, or allowing for 4 consecutive increments but notfive. In certain embodiments, the maximum number of consecutive increasemay be limited to 3, 4, 5, 6, 7, or 8.

When considering a limit to the number of consecutive increments toinsulin dosage a question may arise as to what constitutes an increaseto insulin dosage. For a user of basal insulin only, it is simply thetotal daily amount of insulin that is greater than what was previouslytaken. However, for a patient on premixed insulin or basal-bolus therapythe determination of whether an increase has taken placer may be morecomplex. For example, if a subject is taking 20 units of premixedinsulin at breakfast and 30 units at dinner, and the subject's bloodglucose level is elevated before dinner but low before breakfast, areasonable adjustment to the insulin dosage may be to take 22 units atbreakfast and 27 units at dinner, and it is not necessarily clear ifthis adjustment constitute an increase or a decrease to the insulindosage. Similarly, a user of basal-bolus insulin therapy may be taking10 units of rapid-acting insulin with every meal, a correction factoradding one unit of insulin for every 30 mg/dl of elevated glucose above120 mg/dl, and a bedtime dose of 30 units of long acting insulin.Glucose levels during a certain period may indicate it is reasonable toincrease breakfast dose from 10 units to 12 units, decrease lunch dosefrom 10 units to 9 units, decrease dinner dose from 10 to 8 units, andincrease bedtime dose from 30 to 35 units. Similarly, it is notnecessarily clear that the new dosage constitute an increase compared tothe previous dosage.

A method to examine if insulin dosage has been increased may be based onthe total daily amount of insulin. For example, for the aforementionedpremixed insulin case the first daily total amount of insulin was 50units (20 at breakfast and 30 at dinner) and the daily total amount ofinsulin for the new dosage was 49 (22 at breakfast and 27 at dinner).Therefore the new dosage would not be considered an increase. For theaforementioned basal-bolus example the initial daily total would be 60units (3×10+30) and the new daily total is 64 and therefore would beruled an increase.

Another method to determine if insulin dosage has been increased is ifany component of the dosage is greater than the same component for theprevious dosage. Using this method, in both of the above examples thechange would be considered an increase. That is, the premixed insulinbecause breakfast dose increased from 20 units to 22 units, and thebasal-bolus insulin because breakfast dose increased from 10 to 12 unitsand bedtime increased from 30 to 35 units.

Another method to determine if insulin dosage has been increased can usea “majority voting” rule in which a majority of the dosage componentshave to increase in order to determine the dosage has been increasedcompared to the previous period. Under the majority voting rule neitherone of the previous examples would be considered an increase. That is,the premixed insulin example had one out of two components increased,and the basal-bolus example had two out of four component increased,none of which constitutes a majority.

Thus, there are several ways one can use to assess as to whether the newdosage represents an increment compared to the previous dosage. Someexamples are given below in table 1. This can be simple for basal onlyinsulin regimen where the user has to administer Z₁ insulin units perday. Then, if the new dosage components Z₂ is greater than Z₁ the dosagehas been increased. However, for more complex regimens such aspremixed/biphasic insulin therapy or basal bolus insulin therapyalternative definitions can be used to define what constitutes a dosageincrease. For example, for a premixed/biphasic insulin regimen eachdosage component is simply the dose one needs to administer for a givenevent. That is, a premixed insulin dosage may include a dose of X₁ unitsof insulin at breakfast and a dose of Y₁ units at dinner. If the newdosage includes X₂ and Y₂ then several methods can be used to determinean increment, e.g., the methods shown in Table 1 below:

TABLE 1 X₂ + Y₂ > X₁ + Y₁ X₂ > X₁ and + Y₂ ≥ Y₁ Y₂ > Y₁ and + X₂ ≥ X₁X₂ > X₁ and + Y₂ < Y₁ but X₂ + Y₂ > X₁ + Y₁ Y₂ > Y₁ and + X₂ < X₁ butX₂ + Y₂ > X₁ + Y₁ either X₂ > X₁ or Y₂ > Y₁

In addition, in some embodiments a projected daily total such as the sumof the dosage components may be used to determine whether the currentinsulin dosage represents an increase compared to prior week. Inregimens that require carbohydrate counting, the projected daily totalwould require estimating average meal size as the dosage component areratios and cannot be simply added to determine daily total. Meal sizeestimates can relies on recorded carbohydrate intake logged in thesystem over a predefined period of time, for example, the last week, thelast couple of weeks, or the last month. An estimated meal content canbe calculated for different meals or as a daily total recordedcarbohydrate intake.

In some embodiments, historic data stored in the system memory may beincomplete. In certain embodiments, incomplete data may be defined asless than 3 data points for a particular events. In other embodiments,incomplete data may be defined as less than 5, 4, 2, or 1 data pointsper event. In some instances, the user may chose to only measure fastingblood glucose. This has a varying level of meaning depending on theinsulin regimen used by the user. If a user is following a basal onlyregimen than fasting blood glucose level may be sufficient to safely andeffectively adjust insulin dosage to achieve a better glycemic balance.However, for a person using premixed insulin therapy, that administersinsulin twice a day, a single test per day may not suffice toappropriately adjust insulin.

In certain embodiments, the instance when a particular data set (e.g.events of type ‘Breakfast’) is in complete is referred to as a missingdata set. If there is a missing data set the system may decide to keepinsulin dosage unchanged. In other embodiments, the presence of amissing data set may be a reason to limit the allowed change for otherdosage components. With basal-bolus insulin therapy, the presence of amissing data set may be used to limit the ratio between fast acting andlong acting insulin. In other embodiments, it may be desired to limitthe increase allowed for a single fast-acting insulin dosage component.

Using methods to prevent consistent increase or unstable increase(oscillating) of insulin dosage may lead to a better prognosis for alonger period of time with potentially better outcomes. The exampleshown in FIG. 37 shows how the aforementioned methods are used toconstrain the rate in which insulin dosage increases during the initialphase of therapy, as insulin increase was stopped on 11/23/12 and on1/17/13 despite elevated glucose levels. Similarly, on 4/5/13 the totaldaily amount of insulin was 349 units, it was reduced to 332 units forthe following period, then increased but only to 343 units to preventunstable oscillations of dosage. Combinations of the proposed methodsresulted in the ability to bring glucose levels into the desired rangeand maintain them in the desired range for 6 months.

Balance Point

Clinical data may suggest that the optimal balance point is differentfor different people with diabetes. For example, in some studies it wasnoted that a small portion of the study population (about 10% ofsubjects) experienced about 90% of the severe hypoglycemic episodes. Itis very likely that some people with diabetes are more prone tohypoglycemia. For such individuals the optimal glycemic balance may meanA1C>7%, since A1C of 7% or less will place them at a too greater risk.

The optimal glycemic balance for each individual may vary overtime andthat there may be no ‘steady state’. That is, the optimal GCI for eachindividual may need to be constantly evaluated. One reason for this maybe that GCI may be affected by the variability of an individual glucosedata. For some that variability is low as illustrated in FIG. 13 . FIG.13 illustrates a patient with low variability of glucose levels. Eachpoint of the figure represents weekly mean glucose data and the verticalbars are plus or minus one standard deviation. In others the variabilitymay be high as illustrated in FIG. 14 . FIG. 14 illustrates a patientwith high variability of glucose level. Each point of the figurerepresents weekly mean glucose data and the vertical bars are plus orminus one standard deviation. In others the variability may beinconsistent as illustrated in FIG. 15 . FIG. 15 illustrates a patientwith varying level of glycemic variability. Each point of the figurerepresents weekly mean glucose data and the vertical bars are plus orminus one standard deviation. In weeks 10-16 there are far more glucosevalues<65 mg/dl (the numbers beneath each bar) as compared to weeks 1-9despite the fact that mean glucose is roughly stable and in factslightly higher during the second period.

Overall, in certain embodiments applying a mass policy of optimizing GCImay be much safer than applying a policy aimed at reducing A1C.Neglecting to set therapy goals that accounts for hypoglycemia may leadto severe consequences and even death. However, by applying a policy ofoptimizing glycemic balanced or GCI, as illustrated in certainembodiments, one can be reassured that therapy will be intensify only aslong as it does not lead to too many hypoglycemic episodes.

There is little consensus as to what constitute minor hypoglycemia. Itis generally accepted that severe hypoglycemia is one that requires theassistant of a third party to be resolved. It is also accepted thatminor hypoglycemia may be the best predictor for severe hypoglycemia.However, while some define minor hypoglycemia as capillary glucoselevels below 70 mg/dl numbers such as 65, 50, and even 40 mg/dl can alsobe found.

An example of a subject with a high glucose level and low variability isillustrated in FIGS. 16 and 17 . FIG. 16 shows that throughout the 16weeks period the patient had just one glucose level <65 mg/dl (week 15).FIG. 17 shows that the total daily insulin more than double over 12weeks (120 to 270 [IU]). This particular subject can safely maintain A1Clevel of 6.1%, well below the recommended goal of 7%.

An example of a subject with low glucose with high variability isillustrated in FIGS. 18 and 19 . FIG. 18 illustrates that during thefirst 4 weeks (ran-in period) subject has 3 hypoglycemic episodes perweek (a rate of 156 episodes/year), and during the last 4 weeks thesubject had 4 hypoglycemic episodes, a rate of 52/yr. FIG. 19illustrates that that insulin did not decrease but rather increasedthroughout the intervention period, from ˜80 units a day to nearly 140units a day.

An example of a subject with low glucose with low variability isillustrated in FIGS. 20 and 21 . FIG. 20 illustrates that the subjecthad week 4 A1C of 7.7%, mean glucose is below 150 mg/dl, yet there are 6episodes of hypoglycemia during the run-in period. FIG. 21 shows thatfor this subject the total daily insulin remains fairly stable at ˜180units, yet its distribution is shifted from being 45%/55% basal to bolusin week 4 to being 30%/70% basal to bolus in week 16.

An example of a subject with high glucose with high variability isillustrated in FIGS. 22 and 23 . FIG. 22 illustrates that for thissubject the hypoglycemia rate decreased from 182/yr to 48/yr, while A1Cincreased from 8.2% (week 4) to 9.7% (week 16). FIG. 23 as opposed tothe previous 3 examples, illustrates that this subject countscarbohydrates to figure out his bolus doses. Hence, meal dosage is givenas ratio. For example, dinner dosage starts at a ratio of 1 [IU] toevery 15 grams of carbohydrates and end at a ratio of 1 [IU] to every 9grams of carbohydrates]. To reduce hypoglycemia basal dose is reducedfrom 25 [IU] to 12 [IU] (weeks 4 to 8). Thereafter, without hypoglycemiainsulin dosage is slowly increased. Eventually, the subject is taking atthe end of the study almost the same amount of insulin as in thebeginning yet with a far different distribution.

An example of a patient on premixed insulin therapy is illustrated inFIGS. 24 and 25 . FIG. 24 shows a reduction in weekly mean glucose fromweeks 1-4 ‘for no apparent reason’ as insulin dosage remained unchangedduring that time, then the increase in insulin dosage in weeks 5-8 (froma daily total of 92 units to 140 units) resulted in a significantincrease in mean glucose (from ˜230 mg/dl to ˜300 mg/dl), then in weeks12-14 mean glucose roughly equals that of week 5 although insulin dosageis ˜190 units a day (more than twice that of week 5). FIG. 25illustrates the fact that certain disclosed embodiments did not increasedosage for more than 4 consecutive weeks. Note that there is no dosageincrease in weeks 9 and 13 despite elevated glucose levels.

FIG. 26 and FIG. 27 illustrate that certain disclosed embodiments hadthe ability to adjust different dosage components independently for apatient on premixed therapy. In weeks 6-9 and 12-16 the patient isexperiencing some hypoglycemia throughout the day to which theembodiments respond by reducing the breakfast dosage component while thedinner component may still increase. This patient had week 0 A1C of9.1%, week 4 of 8%, and week 16 of 5.8%.

FIG. 28 illustrates a patient taking a relatively small daily total of˜45 units per day almost equally divided between basal and bolus. Thepatient's mean glucose during the run-in period is just below 150 mg/dl,yet A1C is 9% in week 0 and 8.4% in week 4. Certain embodiments arecapable of further improving glycemic balance by slowly increasing theindependent bolus dosage components to a final daily total of ˜57units/day. Week 16 A1C is improved to 7.4% with only two hypoglycemicepisodes one in each of the final two weeks. FIG. 29 illustrates thatbasal insulin starts at 24 and ends at 25 units/day with a peak of 28units/day for weeks 14-15. At the same time: breakfast dosage goes from6 to 9 (+50%), lunch dosage goes from 6 to 11 (+83%), and dinner dosagegoes from 8 to 11 (+37%). While the bolus dosage increase may seemdramatic it was achieved in a safe manner with acceptable rate ofhypoglycemia and well improved A1C.

These examples illustrate that in certain embodiments the goal ofdiabetes management may be achieving glycemic balance or improvedglycemic composite index weighing both A1C and hypoglycemia.

Certain embodiments of the present disclosure are directed to systems,methods and/or devices for treating a patient's diabetes by providingtreatment guidance based whether and by how much to vary at least one ofthe one or more components in the patient's present insulin dosageregimen to get closer to the patient's desired glycemic balance point.

Certain embodiments of the present disclosure are directed to systems,methods and/or devices for treating a patient's diabetes by providingtreatment guidance based whether and by how much to vary at least one ofthe one or more components in the patient's present insulin dosageregimen to get closer to the patient's desired glycemic balance pointand individual time-varying treatment targets.

Certain embodiments of the present disclosure are directed to systems,methods and/or devices for treating a patient's diabetes by providingtreatment guidance that are designed to slowly and/or safely guide itsuser to a better glycemic balance.

Certain embodiments are directed to providing guidance on a dynamicbasis for each individual subject in order to move the subject anappropriate glycemic balance.

In certain embodiments, treatment of a patients diabetes by providingtreatment guidance using glycemic balance may assumed one or more of thefollowing:

-   a) lowering mean glucose increase the chances of experiencing    hypoglycemia;-   b) hypoglycemia poses a potential risk for the patients and under    certain conditions it should lead to an immediate dosage adjustment    (regardless of the synchronic interval);-   c) a single severe hypoglycemic event may be an outlier. As such, it    requires an immediate attention but does not reset the synchronous    clock;-   d) events may not need to be double counted, in other words, if a    dosage component was adjusted in response to (c) that particular    severe hypoglycemic data point will typically be ignored and not    used again when the synchronic evaluation of the data occurs; and/or-   e) dosage evaluation should typically reflect the current dosage.    That is, when an asynchronous, full, dosage adjustment occurs (due    to an excessive number of hypoglycemic events over since the last    full update) the synchronous clock would be reset and the    hypoglycemic events that caused the asynchronous full dosage    adjustment expire.

The result is that certain embodiments use a varying length window thatcontains the events that occurred after the last update but are no olderthan 7 days (this is done to allow events to expire based on time in thecase that there were not enough events recorded in history to adjustdosage). Other time periods may also be used such as 2, 3, 4, 5, 6, 8,9, 10 11, 12, 13 or 14 days. Certain embodiments may perform at leasttwo types of updates: partial and full.

A partial update may be triggered by a severe hypoglycemic event andimmediately adjusts (reduces) the dosage component that presumablycaused the severe low. It does not have to reset the clock and may betreated as an outlier until there is more evidence that it wasn't anoutlier (i.e., there are more hypoglycemic episodes). In certainembodiments, partial updates are only triggered by severe hypoglycemicepisodes. In certain embodiments any low blood glucose value, lowmeaning below a particular threshold, can lead to either a partial or afull update of the insulin dosage. In certain embodiments two or morelow blood glucose levels can lead to a full update. In certainembodiments one severe hypoglycemic episodes and two low blood glucoselevels may lead to a full update. In other embodiments, low bloodglucose values may only lead to partial updates. In certain embodiments,two or more low blood glucose value per event may lead to a full update.In other embodiments more than 3, 4, or 5 low blood glucose values mayresult in a full update.

A full update uses the available data in the valid history (for example,newer than the last dosage update and not older than 7 days) to adjustone or more dosage components. In certain embodiments, the full updatewill adjust all dosage components. In other embodiments, the full updatewill adjust one or more dosage components. Other time periods may alsobe used such as 2, 3, 4, 5, 6, 8, 9, 10 11, 12, 13 or 14 days. It isassumed that this data set reflects the up-to-date efficacy of theactive dosage. In certain embodiments, a full update resets thesynchronous clock thus causing the events that were part of this dosageadjustment to expire by becoming older than the most recent dosagetimestamp. In certain embodiments, a full update resets the synchronousclock thus causing a substantial portion of the events that were part ofthis dosage adjustment to expire by becoming older than the most recentdosage timestamp. In certain embodiments, a full update resets thesynchronous clock thus causing all of the events that were part of thisdosage adjustment to expire by becoming older than the most recentdosage timestamp. In certain embodiments, a full update resets thesynchronous clock thus causing a portion of the events that were part ofthis dosage adjustment to expire by becoming older than the most recentdosage timestamp.

In certain embodiments, full update may be triggered by time, by thedetermination that frequency of hypoglycemia exceeded certain limits, orcombinations thereof. A full update can also be triggered by externalinterventions, such as by a treating clinician or by incorporatingadditional knowledge that may affect the user metabolic state. Suchknowledge may be that the user has started or discontinued other drugs,or the development of physiological conditions weather temporal sicknesslike the flu or conditions like end stage renal failure. Other examplesthat can trigger a full update are a visit to the emergency room, anysort of trauma injury, or other medical conditions that would leadsomeone knowledgeable in this field to reset insulin dosage or regimenor both.

In certain embodiments, a full update may be triggered by time, forexample, more than 7 days have passed since last update. In such caseseach data set is evaluated for completeness. Certain embodiments requireat least 3 data points per event. If a certain event has less than 3data points it is declared as missing data. If data is missing from oneevent then certain safety measures are applied to make sure that theremaining dosage component are not going to change too aggressively. Forexample, If more than 1 data set is missing then it may be decided notto adjust dosage.

In certain embodiments, full update can also be triggered by thedetermination that frequency of hypoglycemia exceeded certain limits. Insuch cases it is highly likely that there is less than 3 data points perevent. Nonetheless, since the full update was triggered for safetycertain embodiments use whatever data is available in memory.

The logic behind certain embodiments is that a) you have to let a dosagesettle in; and, b) if a full update occurred than the events (includinglow) have to expire otherwise certain embodiments would be accountingfor events that do not reflect the efficacy of the current dosage (i.e.,hypoglycemic episodes that occurred before the active dosage wasinstated).

In certain embodiments one or more of the following may be combined:

-   1) Increasing insulin dosage may be done at a more gradual pace. For    example, certain embodiments may not allow more than 3, 4, 5, or 6    consecutive increases to insulin dosage. This results in slower    increases of dosage which may have longer terms effects: for example    if a subject starts with 50 units a day and mean glucose levels in    the 200s their dosage can increase 20%-25% for several weeks leading    them to a daily total of about 100 units in 4 weeks. While each    change was small the cumulative effect may take time to settle in.    As can be seen in the subject illustrated in FIGS. 16 and 17 mean    glucose is coming down significantly in weeks 9 and 13 although    insulin dosage is unchanged from previous week.-   2) Hypoglycemia is an inherent part of insulin therapy. There is no    need to respond to it either aggressively or conservatively unless    it reflects on the active dosage.-   3) Limited correlation between events. Certain embodiments treat    each event set independently. Correlation between events in some    embodiments is only considered when data is missing.-   4) Certain embodiments make an attempt to prevent unstable    oscillations by limiting an increase that followed a decrease not to    exceed the level that caused the previous decrease.

In certain embodiments, glycemic index (GI) can be defined as theminimum of the average and the median of a particular data set, e.g.,historic blood glucose level tagged as ‘Lunch’ during the period underevaluation. For a regimen of basal-bolus insulin therapy, GI can then beused to adjust the breakfast dosage component in AIDF according to thefollowing table 2 where Δ is a number of insulin units to be added tothe current breakfast dosage component:

TABLE 2 GI Δ fixed meal bolus Δ for carbohydrate counting  0-50 −MAX(1,INT_MIN[0.1*BD(k), MAX(1, INT_MIN[(0.1*BD(k), 0.2*BD(k)]) 0.2*BD(k)])51-80 −MAX(1, INT_MIN[0.05*BD(k) MAX(1, INT_MIN[(0.05*BD(k), 0.1*BD(k)])0.1*BD(k)])  81-135 (0) (0) 136-200 MAX(1, INT_MIN[0.05*BD(k) −MAX(1,INT_MIN[(0.05*BD(k), 0.1*BD(k)]) 0.1*BD(k)]) 201-250 MAX(1,INT_MIN[0.1*BD(k), −MAX(1, INT_MIN[(0.1*BD(k), 0.2*BD(k)]) 0.2*BD(k)])251-300 MAX(1, INT_MIN[0.15*BD(k), −MAX(1, INT_MIN[0.15*BD(k),0.25*BD(k)]) 0.25*BD(k)]) 301+ MAX(1, INT_MIN[0.2*BD(k), −MAX(1,INT_MIN[0.2*BD(k), 0.3*BD(k)]) 0.3*BD(k)])

Wherein for certain embodiments MAX is the maximum of; INT_MIN is theminimal integer within a given range; and BD(k) refers to the breakfastdosage component within the active IDF. Other ranges of can also be usedon the column in the left hand side. For example, GI ranges can be 0-60,61-70, 71-120, 121-180, 181-230, 231-280, and above 281. Other examplesare also valid. It would be understood that if a patient is using afixed breakfast bolus dose of 10 units and GI=140 than the new breakfastdosage component is adjusted to be 11 units. At the same time, if thepatient is using a carbohydrate to insulin ratio for breakfast of 1insulin units to 10 grams of carbohydrates then according to theright-hand column the new dosage component would be a ratio of 1[IU]:9[grams of carbohydrates].

In certain embodiments different tables can be used to adjust differentdosage components. For example while breakfast dosage component may beadjusted according to the example given in above, certain embodimentsmay use the following table 3 to adjust the dinner dosage component.

TABLE 3 GI Δ fixed meal bolus Δ for carbohydrate counting  0-50 −MAX(1,INT_MIN[0.1*DD(k), 0.2 MAX(1, INT_MIN[0.1*DD(k), *DD(k)]) 0.2*DD(k)]) 51-100 −MAX(1, INT_MIN[0.05*DD(k), MAX(1, INT_MIN[0.05*DD(k),0.1*DD(k)]) 0.1*DD(k)]) 101-200 (0) (0) 201-250 MAX(1,INT_MIN[0.05*DD(k), −MAX(1, INT_MIN[0.05*DD(k), 0.1*DD(k)]) 0.1*DD(k)])251-300 MAX(1, INT_MIN[0.1*DD(k), −MAX(1, INT_MIN[0.1*DD(k), 0.2*DD(k)])0.2*DD(k)]) 301+ MAX(1, INT_MIN[0.15*DD(k), −MAX(1, INT_MIN[0.15*DD(k),0.25*DD(k)]) 0.25*DD(k)])Wherein DD(k) refers to the dinner dosage component of the AIDF.

In certain embodiments, yet another tables can be used to adjust thelong acting insulin dosage component. For example while breakfast dosagecomponent or dinner dosage components may be adjusted according to theaforementioned examples, certain embodiments may use the following table4 to adjust the long acting dosage component based on breakfast glucosedata

TABLE 4 GI Δ  0-50 −MAX(1, INT_MIN[0.1*LD(k), 0.2*LD(k)])  51-100−MAX(1, INT_MIN[0.05*LD(k), 0.1*LD(k)]) 101-135 0 136-200 MAX(1,INT_MIN[0.05*LD(k), 0.1*LD(k)]) 201-250 MAX(1, INT_MIN[0.1*LD(k),0.2*LD(k)]) 251-300 MAX(1, INT_MIN[0.15*LD(k), 0.25*LD(k)]) 301+ MAX(1,INT_MIN[0.2*LD(k), 0.3*LD(k)])

Managing Population of Diabetics

In certain embodiments, it is desired to have a group of people withinsulin-treated diabetes better manage their blood glucose levels. Suchembodiments can be used to significantly reduce cost of health care. Forexample, it is well documented that high hemoglobin A1C is acontributing factor to a significantly higher chances of developingdiabetes related complications. Studies have shown that reducing apatient's A1C from 9% to 7% reduces his chances of developingretinopathy by about 76%. As nearly 80% of health care costs are due tohospitalizations, readmissions, or visits to the emergency room, it isuseful to reduce average A1C within a population as a tool to reducecosts of health care. It is also useful not to reduce A1C below acertain threshold as low A1C have been shown to be a high risk factorfor severe hypoglycemia. Since hypoglycemia is the leading cause foremergency room visits for people with insulin treated diabetes, it isuseful to reduce the rate of hypoglycemia of a given population as a wayto reduce overall costs of health care.

In certain embodiments it is desired to enroll a patient population to aservice that adjusts insulin dosage as a way to improve diabetesprognosis by reducing A1C and/or the rate of hypoglycemia leading to areduction in health care costs. For example, enrolling a group ofpatients that are 21-70 years of age and had a clinical diagnosis oftype 1 or type 2 diabetes for at least one year. In this example,patients may be excluded if they have a body mass index (BMI) ≥45 kg/m²;severe impairment of cardiac, hepatic, or renal functions;psychological, or cognitive impairment; more than two episodes of severehypoglycemia in the past year; or a history of hypoglycemia unawareness.Eligible patients can be enrolled into one of 3 treatment groups whichincluded patients with: I. suboptimally controlled type 1 diabetes(A1C≥7.4%) treated with basal-bolus insulin therapy that may incorporatecarbohydrate-counting; II. suboptimally controlled type 2 diabetes(A1C≥7.4%) treated with basal-bolus insulin therapy (withoutcarbohydrate-counting); and III. suboptimally controlled type 2 diabetes(A1C≥7.8%) treated with twice daily biphasic insulin.

In this example, it was useful to use the first 4 weeks as a baselineand allow patients to continue their pre-enrollment regimens withoutintervention. During the following 12 weeks, self-measured blood glucosereadings reported on patients' diaries can be processed weekly bycertain embodiments which recommends a new insulin dosage. Althoughgenerally encouraged to follow dosage recommendations, patients areallowed to deviate from the prescribed dosage during unusual situations(e.g. anticipated physical activity). Patients in Groups I and II areasked to test and record their capillary glucose 4 times a day beforemeals and before bedtime and patients in group III are asked to testtwice a day, before breakfast and dinner. All patients may be asked tomeasure capillary glucose during the night every 5-9 days. Informationcaptured in diaries included time-stamped scheduled and unscheduledglucose readings, insulin doses, and carbohydrate quantities (Group Ionly). Reduction of health care costs is measured by improved efficacy:defined in this example as the improvement in self measured weekly meanglucose, and reduction in A1C; and, by improved safety defined asreduction in the frequency of hypoglycemia for patients suffering from ahigh rate of hypoglycemia, e.g., more than 3 events per week, andmaintaining rate of hypoglycemia at an acceptable level, e.g., no morethan one event per week, for everyone else. In this example,hypoglycemia is defined as a blood glucose <65 mg/dl.

Using certain disclosed embodiments a patient population can be treatedto improve diabetes management and reduce health care costs by providingthem with a device that replaces their glucose meters and automaticallyuses the plurality of historic glucose data to adjust insulin therapysuch that the population reaches a better glycemic balance point.

In this example, using certain disclosed embodiments, can lead tosignificant reduction in A1C in just 12 weeks, for example from abaseline A1C of 8.4% to an A1C of 7.9%, and reduction in weekly meanglucose from a baseline of 174 mg/dl to an endpoint of 163 mg/dl. And,for patient with high frequency of hypoglycemia reducing its rate from3.2 events per week to 1.9 events per week without increasing A1C levelin a statistically significant manner. And, for patients withoutfrequent hypoglycemia reducing A1C from 8.5% at baseline to 7.8%, meanglucose from 182 mg/dl to 155 mg/dl without increasing frequency ofhypoglycemia, of 0.5 events per week at baseline, in a statisticallysignificant manner

Achieving the above results lead to reduction in the number of officevisits and/or the number of calls from patients to health careproviders, leading to short term health care costs saving. Furthermore,maintaining the above results over a period of time can lead tosignificant reduction in the development of diabetes relatedcomplications or visits to the emergency room resulting in a significanthealth care costs reduction.

In this example, reduction in mean glucose is achieved for all membersof the population as seen in FIG. 30 . Using certain disclosedembodiments, significant reduction in mean glucose and hemoglobin A1Ccan be achieved with population members having type 2 diabetes as seenin FIG. 31 and FIG. 32 . Better glycemic balance is achieved by reducingmean glucose for patient without frequent hypoglycemia while increasingmean glucose for patients with frequent hypoglycemia as seen in FIG. 33. Using certain disclosed embodiments it is possible not only to reducethe number of hypoglycemia events but also to shift their distributionsuch that if an hypoglycemic event occurs it is likelier to have ahigher low blood glucose level, for example above 50 mg/dl, as seen inFIG. 34 . In certain embodiments, statistically significant reduction inthe frequency of hypoglycemia is achieved without an increase in A1C,while statistically significant reduction in A1C is achieved without anincrease in the frequency of hypoglycemia, as seen in FIG. 35 . Incertain embodiments it is useful to increase daily total insulin dosageto achieve reduction in A1C.

In certain embodiments it is useful to achieve reduction in thefrequency of hypoglycemia by changing the distribution of insulinbetween different administration points rather than reducing the dailytotal insulin dosage, as seen in FIG. 36 .

Certain embodiments are directed to methods, systems and/or devices fortreating a patient's diabetes by providing treatment guidance whereinthe frequency of hypoglycemic events is reduced without significantlyreducing the total amount of insulin used by the patient. For example, amethod for treating a patient's diabetes by providing treatmentguidance, the method comprising: storing one or more components of thepatient's insulin dosage regimen; obtaining data corresponding to thepatient's blood glucose-level measurements determined at a plurality oftimes; tagging each of the blood glucose-level measurements with anidentifier reflective of when or why the reading was obtained; anddetermining the patient's current glycemic state relative to a desiredbalance point; and determining from at least one of a plurality of thedata corresponding to the patient's blood glucose-level measurementswhether and by how much to vary at least one of the one or morecomponents in the patient's present insulin dosage regimen to get closerto the patient's desired balance point, without significantly reducingthe total amount of insulin used by the patient; wherein the desiredbalance point is the patient's lowest blood glucose-level within apredetermined range achievable before increasing the frequency ofhypoglycemic events above a predetermined threshold.

Certain embodiments are directed to apparatus for improving the healthof a diabetic population, wherein the frequency of hypoglycemic eventsis reduced without significantly reducing the total amount of insulinused by the patient. For example, an apparatus comprising: a processorand a computer readable medium coupled to the processor and collectivelycapable of: (a) storing one or more components of the patient's insulindosage regimen; (b) obtaining data corresponding to the patient's bloodglucose-level measurements determined at a plurality of times; (c)tagging each of the blood glucose-level measurements with an identifierreflective of when or why the reading was obtained; (d) determining thepatient's current glycemic state relative to a desired balance point;and (e) determining from at least one of a plurality of the datacorresponding to the patient's blood glucose-level measurements whetherand by how much to vary at least one of the one or more components inthe patient's present insulin dosage regimen to get closer to thepatient's desired balance point, without significantly reducing thetotal amount of insulin used by the patient; wherein the desired balancepoint is the patient's lowest blood glucose-level within a predeterminedrange achievable before the frequency of hypoglycemic events exceeds apredetermined threshold.

Certain embodiments are directed to methods, systems and/or devices forimproving the health of a diabetic population, wherein the frequency ofhypoglycemic events is reduced without significantly reducing the totalamount of insulin used by the patients. For example, a method forimproving the health of a diabetic population, the method comprising:identifying at least one diabetic patient; treating the a least onediabetic patient to control the patient's blood glucose level; whereinthe patient's blood glucose level is controlled using a device capableof: (a) storing one or more components of the patient's insulin dosageregimen; (b) obtaining data corresponding to the patient's bloodglucose-level measurements determined at a plurality of times; (c)tagging each of the blood glucose-level measurements with an identifierreflective of when or why the reading was obtained; (d) determining thepatient's current glycemic state relative to a desired balance point;and (e) determining from at least one of a plurality of the datacorresponding to the patient's blood glucose-level measurements whetherand by how much to vary at least one of the one or more components inthe patient's present insulin dosage regimen to get closer to thepatient's desired balance point without significantly reducing the totalamount of insulin used by the patient; wherein the desired balance pointis the patient's lowest blood glucose-level within a predetermined rangeachievable before the frequency of hypoglycemic events exceeds apredetermined threshold.

Optimizing Insulin Dosage Over Time

In certain embodiments, the present disclosure comprehends systems,methods, and/or devices for optimizing the insulin dosage regimen indiabetes patients over time—such as in between clinic visits or forperiods of months or years—to thereby enhance diabetes control.

As used herein with respect to certain embodiments, the term “insulindose” means and refers to the quantity of insulin taken on any singleoccasion, while the term “insulin dosage regimen” refers to and meansthe set of instructions (typically defined by the patient's physician orother healthcare professional) defining when and how much insulin totake in a given period of time and/or under certain conditions. Oneconventional insulin dosage regimen comprises several components,including a long-acting insulin dosage component, a plasma glucosecorrection factor component, and a carbohydrate ratio component. Thus,for instance, an exemplary insulin dosage regimen for a patient might beas follows: 25 units of long acting insulin at bedtime; 1 unit offast-acting insulin for every 10 grams of ingested carbohydrates; and 1unit of fast-acting insulin for every 20 mg/dL by which a patient'sblood glucose reading exceeds 120 mg/dL.

Referring to FIG. 1 , which constitutes a generalized schematic thereof,of certain exemplary embodiments more particularly comprises anapparatus 1 having at least a first memory 10 for storing data inputscorresponding at least to one or more components of a patient's presentinsulin dosage regimen (whether comprising separate units of long-actingand short-acting insulin, premixed insulin, etc.) and the patient'sblood-glucose-level measurements determined at a plurality of times, aprocessor 20 operatively connected (indicated at line 11) to the atleast first memory 10, and a display 30 operatively coupled (indicatedat line 31) to the processor and operative to display at leastinformation corresponding to the patient's present insulin dosageregimen. The processor 20 is programmed at least to determine from thedata inputs corresponding to the patient's blood-glucose-levelmeasurements determined at a plurality of times whether and by how muchto vary at least one or the one or more components of the patient'spresent insulin dosage regimen. Such variation, if effected, leads to amodification of the patient's present insulin dosage regimen data asstored in the memory 10, as explained further herein. Thus, the datainputs corresponding to the one or more components of the patient'spresent insulin dosage regimen as stored in the memory device 10 will,at a starting time for employment of the apparatus, constitute aninsulin dosage regimen prescribed by a healthcare professional, butthose data inputs may subsequently be varied by operation of theapparatus (such as during the time interval between a patient's clinicvisits). In the foregoing manner, the apparatus is operative to monitorrelevant patient data with each new input of information (such as, at aminimum, the patient's blood-glucose-level measurements), therebyfacilitating the optimization of the patient's insulin dosage regimen inbetween clinic visits.

It is contemplated that the apparatus as generalized herein may beembodied in a variety of forms, including a purpose-built, PDA-likeunit, a commercially available device such as a cell-phone, IPHONE, etc.Preferably, though not necessarily, such a device would include dataentry means, such as a keypad, touch-screen interface, etc. (indicatedgenerally at the dashed box 40) for the initial input by a healthcareprofessional of data corresponding at least to a patient's presentinsulin dosage regimen (and, optionally, such additional data inputs as,for instance, the patient's present weight, defined upper and lowerpreferred limits for the patient's blood-glucose-level measurements,etc.), as well as the subsequent data inputs corresponding at least tothe patient's blood-glucose-level measurements determined at a pluralityof times (and, optionally, such additional data inputs as, for instance,the patient's present weight, the number of insulin units administeredby the patient, data corresponding to when the patient eats, thecarbohydrate content of the foodstuffs eaten, the meal type (e.g.,breakfast, lunch, dinner, snack, etc.). As shown, such data entry means40 are operatively connected (indicated at line 41) to the memory 10.

Display 30 is operative to provide a visual display to the patient,healthcare professional, etc. of pertinent information, including, byway of non-limiting example, information corresponding to the presentinsulin dosage regimen for the patient, the current insulin dose (i.e.,number of insulin units the patient needs to administer on the basis ofthe latest blood-glucose-level measurement and current insulin dosageregimen), etc. To that end, display 30 is operatively connected to theprocessor 20, as indicated by the dashed line

As noted, the data entry means 40 may take the form of a touch-screen,in which case the data entry means 40 and display 30 may be combined(such as exemplified by the commercially available IPHONE (Apple, Inc.,California)).

Referring then to FIGS. 2 through 5 , there are depicted representativeimages for a display 30 and a touch-screen type, combined display30/data entry means 40 exemplifying both the patient information thatmay be provided via the display, as well as the manner of data entry.

More particularly, FIG. 2 shows a display 30 providing current date/timeinformation 32 as well as the patient's current blood-glucose-levelmeasurement 33 based upon a concurrent entry of that data. FIG. 2further depicts a pair of scrolling arrows 42 by which the patient isable to scroll through a list 34 of predefined choices representing thetime of the patient's said current blood-glucose-level measurement. Asexplained further herein in association with a description of anexemplary algorithm for implementing certain embodiments, selection ofone of these choices will permit the processor to associate themeasurement data with the appropriate measurement time for more precisecontrol of the patient's insulin dosage regimen.

FIG. 3 shows a display 30 providing current date/time information 32, aswell as the presently recommended dose of short-acting insulin units35—based upon the presently defined insulin dosage regimen—for thepatient to take at lunchtime.

FIG. 4 shows a display 30 providing current date/time information 32, aswell as, according to a conventional “carbohydrate-counting” therapy,the presently recommended base (3 IUs) and additional doses (1 IU perevery 8 grams of carbohydrates ingested) of short-acting insulin units36 for the patient to take at lunchtime—all based upon the presentlydefined insulin dosage regimen.

In FIG. 5 , there is shown a display 30 providing current date/timeinformation 32, as well as the presently recommended dose ofshort-acting insulin units 37—based upon the presently defined insulindosage regimen—for the patient to take at lunchtime according to adesignated amount of carbohydrates to be ingested. As further depictedin FIG. 5 , a pair of scrolling arrows 42 are displayed, by which thepatient is able to scroll through a list of predefined meal choices 38,each of which will have associated therewith in the memory a number(e.g., grams) of carbohydrates. When the patient selects a meal choice,the processor is able to determine from the number of carbohydratesassociated with that meal, and the presently defined insulin dosageregimen, a recommended dose of short-acting insulin for the patient totake (in this example, 22 IUs of short-acting insulin for a lunch ofsteak and pasta).

In one embodiment thereof, shown in FIG. 6 , the apparatus as describedherein in respect of FIG. 1 optionally includes a glucose meter(indicated by the dashed box 50) operatively connected (as indicated atline 51) to memory 10 to facilitate the automatic input of datacorresponding to the patient's blood-glucose-level measurements directlyto the memory 10.

Alternatively, it is contemplated that the glucose meter 50′ could beprovided as a separate unit that is capable of communicating (such asvia a cable or wirelessly, represented at line 51′) with the device 1′so as to download to the memory 10′ the patient's blood-glucose-levelmeasurements, such as shown in FIG. 7 .

According to another embodiment, shown in FIG. 8 , the apparatus 1″ maybe combined with an insulin pump 60″ and, optionally, a glucose meter50″ as well. According to this embodiment, the processor 20″ isoperative to determine from at least the patient's blood-glucose-levelmeasurement data (which may be automatically transferred to the memory10″ where the apparatus is provided with a glucose meter 50″, as shown,is connectable to a glucose meter so that these data may beautomatically downloaded to the memory 10″, or is provided with dataentry means 40″ so that these data may be input by the patient) whetherand by how much to vary the patient's present insulin dosage regimen.The processor 20″, which is operatively connected to the insulin pump60″ (indicated at line 61″), is operative to employ the insulin dosageregimen information to control the insulin units provided to the patientvia the pump 60″. Therefore, the processor 20″ and the pump 60″ form asemi-automatic, closed-loop system operative to automatically adjust thepump's infusion rate and profile based on at least the patient'sblood-glucose-level measurements. This will relieve the burden of havingto go to the healthcare provider to adjust the insulin pump's infusionrate and profile, as is conventionally the case. It will be appreciatedthat, further to this embodiment, the insulin pump 60″ may be operativeto transfer to the memory 10″ data corresponding to the rate at whichinsulin is delivered to the patient by the pump according to thepatient's present insulin dosage regimen. These data may be accessed bythe processor 20″ to calculate, for example, the amount of insulin unitsdelivered by the pump to the patient over a predefined period of time(e.g., 24 hours). Such data may thus be employed in certain embodimentsto more accurately determine a patient's insulin sensitivity, plasmaglucose correction factor and carbohydrate ratio, for instance.

Also further to this embodiment, the apparatus 1″ may optionally beprovided with data entry means, such as a keypad, touch-screeninterface, etc. (indicated generally at the dashed box 40″) for entry ofvarious data, including, for instance, the initial input by a healthcareprofessional of data corresponding at least to a patient's presentinsulin dosage regimen (and, optionally, such additional data inputs as,for instance, the patient's present weight, defined upper and lowerpreferred limits for the patient's blood-glucose-level measurements,etc.), as well as the subsequent data inputs corresponding at least tothe patient's blood-glucose-level measurements determined at a pluralityof times (to the extent that this information is not automaticallytransferred to the memory 10″ from the blood glucose meter 50″) and,optionally, such additional data inputs as, for instance, the patient'spresent weight, the number of insulin units administered by the patient,data corresponding to when the patient eats, the carbohydrate content ofthe foodstuffs eaten, the meal type (e.g., breakfast, lunch, dinner,snack), etc.

It is also contemplated that certain embodiments may be effected throughthe input of data by persons (e.g., patient and healthcare professional)at disparate locations, such as illustrated in FIG. 9 . For instance, itis contemplated that the data inputs pertaining to at least thepatient's initial insulin dosage regimen may be entered by thehealthcare professional at a first location, in the form of a generalpurpose computer, cell phone, IPHONE, or other device 100 (a generalpurpose computer is depicted), while the subsequent data inputs (e.g.,patient blood-glucose-level readings) may be entered by the patient at asecond location, also in the form of a general purpose computer, cellphone, IPHONE, or other device 200 (a general purpose computer isdepicted), and these data communicated to a third location, in the formof a computer 300 comprising the at least first memory and theprocessor. According to this embodiment, the computers 100, 200, 300 maybe networked in any known manner (including, for instance, via theinternet). Such networking is shown diagrammatically via lines 101 and201. Thus, for instance, the system may be implemented via a healthcareprofessional/patient accessible website through which relevant data areinput and information respecting any updates to the predefined treatmentplan are communicated to the patient and healthcare professional.

Alternatively, it is contemplated that certain embodiments may beeffected through the input of data via persons (e.g., patient andhealthcare professional) at disparate locations, and wherein further oneof the persons, such as, in the illustrated example, the patient, is inpossession of a single device 200′ comprising the processor and memorycomponents, that device 200′ being adapted to receive data inputs from aperson at a disparate location. FIG. 10 . This device 200′ could takeany form, including a general-purpose computer (such as illustrated), aPDA, cell-phone, purpose-built device such as heretofore described, etc.According to this embodiment, it is contemplated that the data inputspertaining to at least the patient's initial insulin dosage may beentered (for instance by the healthcare professional) at anotherlocation, such as via a general purpose computer, cell phone, or otherdevice 100′ (a general purpose computer is depicted) operative totransmit data to the device 200′, while the subsequent data inputs(e.g., patient blood-glucose-level measurements) may be entered directlyinto the device 200′. According to this embodiment, a healthcareprofessional could remotely input the patient's initial insulin dosageat a first location via the device 100′, and that data could then betransmitted to the patient's device 200′ where it would be received andstored in the memory thereof. According to a further permutation of thisembodiment, the afore described arrangement could also be reversed, suchthat the patient data inputs (e.g., patient blood-glucose-levelmeasurements) may be entered remotely, such as via a cell phone,computer, etc., at a first location and then transmitted to a remotelysituated device comprising the processor and memory components operativeto determine whether and by how much to vary the patient's presentinsulin dosage regimen. According to this further permutation,modifications to the patient's insulin dosage effected by operation ofcertain embodiments could be transmitted back to the patient via thesame, or alternate, means.

Referring again to FIG. 9 , it is further contemplated that there may beprovided a glucose meter 50′ (including, for instance, in the form ofthe device as described above in reference to FIG. 6 ) that caninterface 51″′ (wirelessly, via a hard-wire connection such as a USBcable, FIREWIRE cable, etc.) with a general purpose computer 200 at thepatient's location to download blood-glucose-level measurements fortransmission to the computer 300 at the third location. Referring alsoto FIG. 10 , it is further contemplated that this glucose meter 50′ maybe adapted to interface 51″′ (wirelessly, via a hard-wire connectionsuch as a USB cable, FIREWIRE cable, etc.) with the single device 200′,thereby downloading blood-glucose-level measurement data to that devicedirectly.

Turning now to FIG. 11 , there is shown a diagram generalizing themanner in which the certain embodiments may be implemented to optimize adiabetes patient's insulin dosage regimen.

In certain embodiments, there is initially specified, such as by ahealthcare professional, a patient insulin dosage regimen (comprised of,for instance, a carbohydrate ratio (“CHR”), a long-acting insulin dose,and a plasma glucose correction factor). Alternatively, the initialinsulin dosage regimen can be specified using published protocols forthe initiation of insulin therapy, such as, for example, the protocolspublished by the American Diabetes Association on Oct. 22, 2008. Howeverspecified, this insulin dosage regimen data is entered in the memory ofan apparatus (including according to several of the embodimentsdescribed herein), such as by a healthcare professional, in the firstinstance and before the patient has made any use of the apparatus.

Thereafter, the patient will input, or there will otherwiseautomatically be input (such as by the glucose meter) into the memory atleast data corresponding to each successive one of the patient'sblood-glucose-level measurements. Upon the input of such data, theprocessor determines, such as via the algorithm described herein,whether and by how much to vary the patient's present insulin dosageregimen. Information corresponding to this present insulin dosageregimen is then provided to the patient so that he/she may adjust theamount of insulin they administer.

According to certain exemplary embodiments, determination of whether andby how much to vary a patient's present insulin dosage regimen isundertaken both on the basis of evaluations conducted at predefined timeintervals (every 7 days, for example) as well as asynchronously to suchintervals. The asynchronous determinations will evaluate the patient'sblood-glucose-level data for safety each time a new blood-glucose-levelmeasurement is received to determine whether any urgent action,including any urgent variation to the patient's present insulin dosage,is necessary.

More particularly, each time a new patient blood-glucose-levelmeasurement is received 300 into the memory it is accessed by theprocessor and sorted and tagged according to the time of day themeasurement was received and whether or not it is associated with acertain event, e.g., pre-breakfast, bedtime, nighttime, etc. 310. Onceso sorted and tagged, the new and/or previously recordedblood-glucose-level measurements are subjected to evaluation for theneed to update on the basis of the passage of a predefined period oftime 320 measured by a counter, as well as the need to updateasynchronously for safety 330. For instance, a very low blood glucosemeasurement (e.g., below 50 mg/dL) representing a severe hypoglycemicevent or the accumulation of several low measurements in the past fewdays may lead to an update in the patient's insulin dosage regimenaccording to the step 330, while an update to that regimen may otherwisebe warranted according to the step 320 if a predefined period of time(e.g., 7 days) has elapsed since the patient's insulin dosage regimenwas last updated. In either case, the patient will be provided withinformation 340 corresponding to the present insulin dosage regimen(whether or not it has been changed) to be used in administering his/herinsulin.

Referring next to FIG. 12 , there is shown a flowchart that still moreparticularly sets forth an exemplary algorithm by which certainembodiments may be implemented to optimize a diabetes patient's insulindosage regimen. According to the exemplary algorithm, the insulin dosagemodification contemplates separate units of long-acting and short-actinginsulin. However, it will be appreciated that certain embodiments areequally applicable to optimize the insulin dosage regimen of a patientwhere that dosage is in another conventional form (such as pre-mixedinsulin). It will also be understood from this specification thatcertain embodiments may be implemented otherwise than as particularlydescribed herein below.

According to a first step 400, data corresponding to a patient's newblood-glucose-level measurement is input, such as, for instance, by anyof the exemplary means mentioned above, into the at least first memory(not shown in FIG. 12 ). This data is accessed and evaluated (by theprocessor) at step 410 of the exemplary algorithm and sorted accordingto the time it was input.

More particularly according to this step 410, the blood-glucose-levelmeasurement data input is “tagged” with an identifier reflective of whenthe reading was input; specifically, whether it is a morning (i.e.,“fast”) measurement (herein “MPG”), a pre-lunch measurement (herein“BPG”), a pre-dinner measurement (herein “LPG”), a bedtime measurement(herein “BTPG”), or a nighttime measurement (herein “NPG”).

The “tagging” process may be facilitated using a clock internal to theprocessor (such as, for instance, the clock of a general purposecomputer) that provides an input time that can be associated with theblood-glucose-level measurement data synchronous to its entry.Alternatively, time data (i.e., “10:00 AM,” “6:00 PM,” etc.) orevent-identifying information (i.e., “lunchtime,” “dinnertime,”“bedtime,” etc.) may be input by the patient reflecting when theblood-glucose-level measurement data was taken, and such informationused to tag the blood-glucose-level measurement data. As a furtheralternative, and according to the embodiment where theblood-glucose-level measurement data are provided directly to the memoryby a glucose monitor, time data may be automatically associated with theblood-glucose-level measurement data by such glucose monitor (forinstance, by using a clock internal to that glucose monitor). It is alsocontemplated that, optionally, the user/patient may be queried (forinstance at a display) for input to confirm or modify any time-tagautomatically assigned a blood-glucose-level measurement data-input.Thus, for instance, a patient may be asked to confirm (via data entrymeans such as, for example, one or more buttons or keys, a touch-screendisplay, etc.) that the most recently input blood-glucose-levelmeasurement data reflects a pre-lunch (BPG) measurement based on thetime stamp associated with the input of the data. If the patientconfirms, then the BPG designation would remain associated with themeasurement. Otherwise, further queries of the patient may be made todetermine the appropriate time designation to associate with themeasurement.

It will be understood that any internal clock used to tag theblood-glucose-level measurement data may, as desired, be user adjustableso as to define the correct time for the time zone where the patient islocated.

Further according to the exemplary embodiment, the various categories(e.g., DPG, MPG, LPG, etc.) into which the blood-glucose-levelmeasurement data are more particularly sorted by the foregoing “tagging”process are as follows:

-   -   NPG—The data are assigned this designation when the time stamp        is between 2 AM and 4 AM.    -   MPG—The data are assigned this designation when the time stamp        is between 4 AM and 10 AM.    -   BPG—The data are assigned this designation when the time stamp        is between 10 AM and 3 PM.    -   LPG—The data are assigned this designation when the time stamp        is between 3 PM and 9 PM.    -   BTPG—The data are assigned this designation when the time stamp        is between 9 PM and 2 AM. If the BTPG data reflect a time more        than three hours after the patient's presumed dinnertime        (according to a predefined time window), then these data are        further categorized as a dinner compensation blood-glucose-level        (herein “DPG”).

According to the employment of a time stamp alone to “tag” theblood-glucose-level data inputs, it will be understood that there existsan underlying assumption that these data were in fact obtained by thepatient within the time-stamp windows specified above.

Per the exemplary embodiment, if the time stamp of a blood-glucose-levelmeasurement data-input is less than 3 hours from the measurement thatpreceded the last meal the patient had, it is considered biased andomitted unless it represents a hypoglycemic event.

According to the next step 420, the newly input blood-glucose-levelmeasurement is accessed and evaluated (by the processor) to determine ifthe input reflects a present, severe hypoglycemic event. This evaluationmay be characterized by the exemplary formula PG(t)<w, where PG(t)represents the patient's blood-glucose-level data in mg/dL, and wrepresents a predefined threshold value defining a severe hypoglycemicevent (such as, by way of non-limiting example, 50 mg/dL).

If a severe hypoglycemic event is indicated at step 420 then, accordingto the step 430, the patient's present insulin dosage regimen data (inthe memory 10 [not shown in FIG. 12 ]) is updated as warranted andindependent of the periodic update evaluation described further below.More particularly, the algorithm will in this step 430 asynchronously(that is, independent of the periodic update evaluation) determinewhether or not to update the patient's insulin dosage regimen on thebasis of whether the patient's input blood-glucose-level data reflectthe accumulation of several low glucose values over a short period oftime. According to the exemplary embodiment, the dosage associated withthe newly input blood-glucose-level measurement is immediatelydecreased. More specifically, for a severe hypoglycemic event at MPG,the long-acting insulin dosage is decreased by 20%; and for a severehypoglycemic event at BPG the breakfast short-acting insulin dose isdecreased by 20%.

The algorithm also at this step 430 updates a counter of hypoglycemicevents to reflect the newly-input (at step 400) blood-glucose-levelmeasurement. Notably, modifications to the patient's insulin dosageregimen according to this step 430 do not reset the timer counting tothe next periodic update evaluation. Thus, variation in the patient'sinsulin dosage regimen according to this step 430 will not prevent thealgorithm from undertaking the next periodic update evaluation.

Any such blood-glucose-level measurement is also entered into ahypoglycemic events database in the memory. In the exemplary embodiment,this is a rolling database that is not reset. Instead, the recordedhypoglycemic events expire from the database after a predefined periodof time has elapsed; essentially, once these data become irrelevant tothe patient's insulin dosage regime. Thus, by way of example only, thisdatabase may contain a record of a hypoglycemic event for 7 days.

Further according to the step 430, one or more warnings may be generatedfor display to the patient (such as via a display 30 [not shown in FIG.12 ]). It is contemplated that such one or more warnings would alert apatient to the fact that his/her blood-glucose-level is dangerously lowso that appropriate corrective steps (e.g., ingesting a glucose tablet)could be taken promptly. Additionally, and without limitation, such oneor more warnings may also correspond to any one or more of the followingdeterminations:

That the patient's blood-glucose-level measurement data reflect thatthere have been more than two hypoglycemic events during a predeterminedperiod of time (such as, by way of example only, in the past 7 days);that more than two drops in the patient's blood-glucose-levelmeasurements between the nighttime measurement and the morningmeasurement are greater than a predetermined amount in mg/dL (70 mg/dL,for instance); and/or that more than two drops in the patient'sblood-glucose-level measurement between the nighttime measurement andthe morning measurement are greater than a predetermined percentage(such as, for instance, 30%).

If a severe hypoglycemic event is not indicated at step 420, therecorded (in the memory 10) data inputs corresponding to the number ofpatient hypoglycemic events over a predetermined period of days areaccessed and evaluated by the processor (20, not shown) at step 440 todetermine if there have been an excessive number of regular hypoglycemicevents (e.g., a blood-glucose-level measurement between 50 mg/dL and 75mg/dL) over that predetermined period. This evaluation is directed todetermining whether the patient has experienced an excessive number ofsuch regular hypoglycemic events in absolute time and independent of theperiodic update operation as described elsewhere herein. Thisassessment, made at step 440, may be described by the following,exemplary formula:Is(#{of events at HG}>Q) or is (#{of hypoglycemic events in the last Wdays}=Q)?

where HG represents the recorded number of hypoglycemic events, W is apredefined period of time (e.g., 3 days), and Q is a predefined numberdefining an excessive number of hypoglycemic events (e.g., 3). By way ofexample, Q may equal 3 and W may also equal 3, in which case if it isdetermined in step 440 that there were either 4 recorded hypoglycemicevents or there were 3 recorded hypoglycemic events in the last 3 days,the algorithm proceeds to step 430.

Notably, if step 440 leads to step 430, then a binary (“1” or “0”)hypoglycemic event correction “flag” is set to “1,” meaning that noincrease in the patient's insulin dosage regimen is allowed and thealgorithm jumps to step 490 (the periodic dosage update evaluationroutine). Potentially, the periodic update evaluation may concur thatany or all the parts of the insulin dosage regimen require an increasedue to the nature of blood-glucose levels currently stored in the memory10 and the execution of the different formulas described hereafter.However, by setting the hypoglycemic event correction flag to “1,” thealgorithm will ignore any such required increase and would leave thesuggested part of the dosage unchanged. Therefore, this will lead to apotential reduction in any or all the components of the insulin dosageregimen to thus address the occurrence of the excessive number ofhypoglycemic events. Further according to this step, the timer countingto the next periodic update evaluation is reset.

In the next step 450, the recorded, time-sorted/taggedblood-glucose-level measurement data corresponding to the number ofpatient hypoglycemic events over a predetermined period of days (forexample, 7 days) are accessed and evaluated by the processor todetermine if there have been an excessive number of such hypoglycemicevents at any one or more of breakfast, lunch, dinner and/or in themorning over the predetermined period. This evaluation may becharacterized by the exemplary formula: #{HG(m)(b)(l)(d) in XX[d]}=Y?;where #HG(m)(b)(l)(d) represents the number of hypoglycemic events atany of the assigned (by the preceding step) measurement times ofmorning, bedtime, lunch or dinner over a period of XX (in the instantexample, 7) days (“[d]”), and Y represents a number of hypoglycemicevents that is predetermined to constitute a threshold sufficient tomerit adjustment of the patient's insulin dosage regimen (in the presentexample, 2 hypoglycemic events). It will be appreciated that theemployment of this step in the algorithm permits identification withgreater specificity of possible deficiencies in the patient's presentinsulin dosage regimen. Moreover, the further particularization of whenhypoglycemic events have occurred facilitates time-specific (e.g., afterlunch, at bedtime, etc.) insulin dosage regimen modifications.

If an excessive number of such hypoglycemic events is not indicated atstep 450, then the algorithm queries at step 460 whether or not it istime to update the patient's insulin dosage regimen irrespective of thenon-occurrence of hypoglycemic events, and based instead upon thepassage of a predefined interval of time (e.g., 7 days) since the needto update the patient's insulin dosage regimen was last assessed. Ifsuch an update is not indicated—i.e., because an insufficient timeinterval has passed—then no action is taken with respect to thepatient's insulin dosage and the algorithm ends (indicated by the arrowlabeled “NO”) until the next blood-glucose-level measurement data areinput.

If, however, an update is indicated by the fact that it has been 7 days(or other predefined interval) since the need to update the patient'sinsulin dosage was last evaluated, then before such update is effectedthe algorithm first determines, in step 470, if the patient's generalcondition falls within a predetermined “normal” range. Thisdetermination operation may be characterized by the exemplary formula:xxx≤5E{PG}≤yyy; where xxx represents a lower bound for a desiredblood-glucose-level range for the patient, yyy represents an upper boundfor a desired blood-glucose-level range for the patient, and E{PG}represents the mean of the patient's recorded blood-glucose-levelmeasurements. According to the exemplary embodiment, the lower bound xxxmay be predefined as 80 mg/dL, and the upper bound yyy may be predefinedas 135 mg/dL.

It will be understood that the foregoing values may be other than as sospecified, being defined, for instance, according to the particularcountry in which the patient resides. Furthermore, it is contemplatedthat the upper (yyy) and lower (xxx) bounds may be defined by thepatient's healthcare professional, being entered, for instance, via dataentry means such as described elsewhere herein.

Where the patient's general condition is outside of the predetermined“normal” range, the algorithm proceeds to step 490 where the data areevaluated to determine whether it is necessary to correct the patient'slong-acting insulin dosage regimen.

Where, however, the patient's general condition is within thepredetermined “normal” range, the algorithm next (step 480) querieswhether the patient's recorded blood-glucose-level measurement datarepresent a normal (e.g., Gaussian) or abnormal distribution. This maybe characterized by the exemplary formula: −X<E{PG{circumflex over( )}3}<X; where E{PG{circumflex over ( )}3} represents the third momentof the distribution of the recorded (in the memory) blood-glucose-levelmeasurement data—i.e., the third root of the average of the cubeddeviations in these data around the mean of the recorded blood-glucoselevels, and X represents a predefined limit (e.g., 5). It iscontemplated that the predefined limit X should be reasonably close to0, thus reflecting that the data (E{PG{circumflex over ( )}3}) are wellbalanced around the mean.

Thus, for example, where X is 5, the data are considered to be normalwhen the third root of the average of the cubed deviations thereofaround the mean of the recorded blood-glucose levels is greater than −5but less than 5. Otherwise, the data are considered to be abnormal.

Where the data are determined to be normal in step 480 (indicated by thearrow labeled “YES”), then no action is taken to update the patient'sinsulin dosage regimen.

However, if in step 470 the mean of all of a patient's recordedblood-glucose-level measurement data are determined to fall outside ofthe predetermined “normal” range, then in step 490 the algorithmevaluates whether it is necessary to correct the patient's long-actinginsulin dosage regimen. This is done by evaluating whether the patient'srecorded MPG and BTPG data fall within an acceptable range or,alternatively, if there is an indication that the patient's long-actinginsulin dosage should be corrected due to low MPG blood-glucose-levelmeasurements. The determination of whether the patient's MPG and BTPGdata fall within a predetermined range may be characterized by theexemplary formula: xxy≤E{MPG}, E{BTPG}≤yyx; where xxy is a lower boundfor a desired blood-glucose-level range for the patient, yyx is an upperbound for a desired blood-glucose-level range for the patient, E{MPG}represents the mean of the patient's recorded MPG blood-glucose-levelmeasurements, and E{BTPG} represents the mean of the patient's recordedBTPG measurements. According to the exemplary embodiments, xxy may bepredefined as 80 mg/dL, while yyx may be predefined as 200 mg/dL.However, it will be understood that these values may be otherwisepredefined, including, as desired, by the patient's healthcare provider(being entered into the memory via data entry means, for instance).

If the determination in step 490 is positive, then update of thepatient's long-acting insulin dosage (step 510) is bypassed and thealgorithm proceeds to step 500, according to which the patient'sshort-acting insulin dosage (in the form of the carbohydrate ratio(“CHR”), a correction factor Δ, and the plasma glucose correction factorare each updated and the hypoglycemic correction “flag” reset to 0 (thuspermitting subsequent modification of the insulin dosage regimen at thenext evaluation thereof).

If, on the other hand, the determination in step 490 is negative, thenthe patient's long-acting insulin dosage is updated at step 510, alongwith performance of the updates specified at step 500. In either case,the process ends following such updates until new patientblood-glucose-level measurement data are input.

Updates of the long-acting insulin dosage regimen data may becharacterized by the following, exemplary formulas:

${\Delta_{up} = {{\left( {1 - {\alpha(2)}} \right){floor}\left\{ \frac{{\alpha(1)}{{LD}(k)}}{100} \right\}} + {{\alpha(2)}{ceil}\left\{ \frac{{\alpha(1)}{{LD}(k)}}{100} \right\}}}}{\Delta_{down} = {{\left( {1 - {\alpha(2)}} \right){floor}\left\{ \frac{{\alpha(1)}{{LD}(k)}}{200} \right\}} + {{\alpha(2)}{ceil}\left\{ \frac{{\alpha(1)}{{LD}(k)}}{200} \right\}}}}{{{If}E\left\{ {MPG} \right\}} < b_{1}}{{{LD}\left( {k + 1} \right)} = {{{LD}(k)} - \Delta_{down}}}{Else}{{{If}E\left\{ {MPG} \right\}} > b_{2}}{{{LD}\left( {k + 1} \right)} = {{{LD}(k)} + \Delta_{up}}}{{{Else}{if}E\left\{ {MPG} \right\}} > b_{3}}{{{LD}\left( {k + 1} \right)} = {{{LD}(k)} + \Delta_{down}}}{End}{End}$where α(1) represents a percentage by which the patient's presentlong-acting insulin dosage regimen is to be varied, α(2) represents acorresponding binary value (due to the need to quantize the dosage),LD(k) represents the patient's present dosage of long-acting insulin,LD(k+1) represents the new long-acting insulin dosage, b₁, b₂, and b₃represent predetermined blood-glucose-level threshold parameters inmg/dL, and E{MPG} is the mean of the patient's recorded MPGblood-glucose-level measurements.

Since a patient's insulin dosage regimen is expressed in integers (i.e.,units of insulin), it is necessary to decide if a percent change(increase or decrease) in the present dosage regimen of long-actinginsulin that does not equate to an integer value should be the nearesthigher or lower integer. Thus, for instance, if it is necessary toincrease by 20% a patient's long-acting insulin dosage regimen from apresent regimen of 18 units, it is necessary to decide if the new dosageshould be 21 units or 22 units. In the exemplary algorithm, thisdecision is made on the basis of the patient's insulin sensitivity.

Insulin sensitivity is generally defined as the average total number ofinsulin units a patient administer per day divided by the patient weightin kilograms. More particularly, insulin sensitivity (IS(k)) accordingto the exemplary algorithm may be defined as a function of twice thepatient's total daily dosage of long-acting insulin (which may bederived from the recorded data corresponding to the patient's presentinsulin dosage regimen) divided by the patient's weight in kilograms.This is expressed in the following exemplary formula:

${{IS}(k)} = \frac{2 \cdot {{LD}(k)}}{KK}$where KK is the patient weight in kilograms.

A patient's insulin sensitivity factor may of course be approximated byother conventional means, including without reliance on entry of datacorresponding to the patient's weight.

More particularly, the exemplary algorithm employs an insulinsensitivity correction factor (α_((2x1))(IS)))), a 2 entries vector, todetermine the percentage at which the dosage will be corrected and toeffect an appropriate rounding to the closest whole number for updatesin the patient's CHR, PGR and LD. When the patient's weight is known,this determination may be characterized by the following, exemplaryformula:

${\alpha({IS})} = \left\{ \begin{matrix}{\left\lbrack {50} \right\rbrack^{\prime},} & {{{IS}(k)} < y_{1}} \\{\left\lbrack {100} \right\rbrack^{\prime},} & {y_{1} \leq {{IS}(k)} < y_{2}} \\{\left\lbrack {200} \right\rbrack^{\prime},} & {y_{2} \leq {{IS}(k)} < y_{3}} \\{\left\lbrack {201} \right\rbrack^{\prime},} & {y_{3} \leq {{IS}(k)}}\end{matrix} \right.$where α(1) is a percentage value of adjustment from the present to a newinsulin dosage value, and α(2) is a binary value (i.e., 0 or 1). Thevalue of α(2) is defined by the value of IS(k) in relation to apredefined percent change value (e.g., y₁, y₂, y₃, y₄) for α(1). Thus,in the exemplary embodiment of the algorithm: Where, for example,IS(k)<0.3, the value of α(1) is 5 and the value of α(2) is 0; where0.3≤IS(k)<0.5, the value of α(1) is 10 and the value of α(2) is 0; where0.5≤IS(k)<0.7, the value of α(1) is 20 and the value of α(2) is 0; andwhere 0.7≤IS(k), the value of α(1) is 20 and the value of α(2) is 1.

When the patient weight is unknown, the algorithm will determine a usingthe following alternative: α(2) is set to “1” if the patient long actinginsulin dosage is greater than X units (where, for example X may equal50 insulin units), and the percentage by which we adjust the dosage willbe determined according to the mean of the blood-glucose-levelmeasurements currently in memory (i.e., E{PG}) by:

${\alpha(1)} = \left\{ \begin{matrix}{5,} & {w_{1} \leq {E\left\{ {PG} \right\}} < w_{2}} \\{10,} & {w_{2} \leq {E\left\{ {PG} \right\}} < w_{3}} \\{20,} & {w_{3} \leq {E\left\{ {PG} \right\}}}\end{matrix} \right.$where w₁, w₂ and w₃ each represent a predefined blood-glucose-levelexpressed in mg/dL (thus, for example, w₁ may equal 135 mg/dL, w₂ mayequal 200 mg/dL, and w₃ may equal 280 mg/dL).

Returning to the exemplary formulas for updating the patient'slong-acting insulin dosage, in the exemplary algorithm the decision ofwhether and by how much to decrease or increase a patient's long-actinginsulin dosage regimen is based on the predetermined thresholdparameters b₁, b₂, and b₃; where, by way of example only, b₁=80 mg/dL,b₂=120 mg/dL, and b₃=200 mg/dL. More particularly, where the mean of thepatient's MPG blood-glucose-level data is less than 80 mg/dL, the newlong-acting insulin dosage (LD(k+1)) is the present long-acting insulindosage (LD(k)) minus the value of Δ_(down) (which, as shown above, is afunction of the insulin sensitivity correction factors α(1) and α(2),and the patient's long-acting insulin dosage (LD(k)) and may equal halfof Δ.sub.up). Otherwise, if the mean of the patient's MPGblood-glucose-level data is greater than 200 mg/dL, the new long-actinginsulin dosage (LD(k+1)) is the present long-acting insulin dosage(LD(k)) plus the value of the Δ_(up) (which, as shown above, is afunction of the insulin sensitivity correction factors α(1) and α(2),and the patient's long-acting insulin dosage (LD(k)). Finally, if themean of the patient's MPG blood-glucose-level data is greater than 150but less than 200, the new long-acting insulin dosage (LD(k+1)) is thepresent long-acting insulin dosage (LD(k)) plus the value of theΔ_(down).

The corrective amount Δ is calculated as a percentage of the currentlong-acting insulin dosage rounded according to α(2). In a particularexample, if α(1)=20, α(2)=0, and the current long acting insulin dosageLD(k)=58, then Δ.sub.up equals 20% of 58, which is 11.6, rounded down toΔ_(up)=11. Accordingly, the long-acting insulin dosage would be updatedto LD(k+1)=58+11=69.

It will be appreciated by reference to the foregoing that in certainembodiments no “ping-pong” effect is allowed; in other words, thepatient's long-acting insulin dosage may not be adjustable so that anytwo successive such adjusted dosages fall below and above the dosagewhich they immediately succeed. Thus, it is not permitted to have theoutcome where the latest LD update (LD(2)) is greater than the initialLD set by the healthcare professional (LD(0)), and the preceding LDupdate (LD(1)) is less than LD(0). Thus, the outcome LD(2)>LD(0)>LD(1)is not permitted in certain embodiments.

Returning to the step 450, if an excessive number of hypoglycemic eventsat any of the time-tagged blood-glucose-level measurement data forbreakfast, lunch, dinner or in the morning over the predetermined period(for instance, 7 days) are indicated from the patient's data, then atstep 520 the algorithm identifies from the recorded, time-tagged data ofhypoglycemic events when those events occurred in order to affect anysubsequently undertaken variation to the patient's insulin dosageregimen, and also sets the binary hypoglycemic correction “flag” (e.g.,“1” or “0”, where 1 represents the occurrence of too many hypoglycemicevents, and 0 represents the nonoccurrence of too many hypoglycemicevents) to 1. The presence of this “flag” in the algorithm at thisjuncture prevents subsequent increases in the patient's insulin dosageregimen in the presence of too many hypoglycemic events.

Further according to this step 520, where the blood-glucose-levelmeasurement data reflects hypoglycemic events in the morning or duringthe night, the algorithm identifies the appropriate modificationrequired to any subsequent variation of the patient's insulin dosageregimen. This may be characterized by the following, exemplary formula:If #HG events in {MPG+NTPG}=X, then reduce LD by α(1)/2; where #HG isthe number of recorded patient hypoglycemic events at the MPG andNTPG-designated blood-glucose-level measurements, X is a predefinedvalue (such as, for example, 2), LD refers to the long-acting insulindosage, and α(1) represents the afore described insulin sensitivitycorrection factor, expressed as a percentage. Thus, α(1)/2 reflects thatthe patient's long-acting insulin dosage is to be reduced only by ½ ofthe value of α(1), if at all, where the recorded hypoglycemic eventsoccur in the morning or overnight.

Further according to this step 520, where the blood-glucose-levelmeasurement data reflects hypoglycemic events during the day, thealgorithm identifies the appropriate modification required to anysubsequent variation of the patient's insulin dosage regimen. This maybe characterized by the following formula: If #HG events in {BPG or LPGor NTPG}=X, then see update 6; where #HG is the number of recordedpatient hypoglycemic events at any of the BPG, LPG or NTPG time-taggedmeasurements, X is a predefined value (for instance, 2), and “see updateΔ” refers to short-acting insulin dosage correction factor Δincorporated into the exemplary form of the algorithm, as describedherein.

Following step 520, the algorithm queries 530 whether it is time toupdate the patient's insulin dosage regimen irrespective of theoccurrence of hypoglycemic events and based upon the passage of apredefined interval of time (by way of non-limiting example, 7 days)since the need to update the patient's insulin dosage regimen was lastassessed. Thus, it is possible that a patient's insulin dosage regimenwill not be updated even though the HG correction flag has been“tripped” (indicating the occurrence of too many hypoglycemic events) ifan insufficient period of time has passed since the regimen was lastupdated.

If an insufficient period of time has passed, the process is at an end(indicated by the arrow labeled “NO”) until new blood-glucose-levelmeasurement data are input. If, on the other hand, the predefined periodof time has passed, then the algorithm proceeds to the step 490 todetermine if the long-acting insulin dosage has to be updated asdescribed before followed by the update step 500, according to which thepatient's short-acting insulin dosage (in the form of the carbohydrateratio (“CHR”)), the correction factor Δ, and plasma glucose correctionfactor are each updated and the hypoglycemic correction flag reset to 0.

According to the step 500, an update to the patient's plasma glucosecorrection factor (“PGR”) is undertaken. This may be characterized bythe following, exemplary formulas:

${{{{Calculate}{new}{{PGR}\left( {``{NPGR}"} \right)}:{NPGR}} = {{\frac{1700}{E\left\{ {DT} \right\}}.{Calculate}}{difference}}},{\Delta = {❘{{{PGR}(k)} - {NPGR}}❘}}}{{{If}\frac{\Delta}{{PGR}(k)}} \leq \frac{\alpha(1)}{100}}{\text{~~}{\Delta = {{\left( {1 - {\alpha(2)}} \right){floor}\left\{ \Delta \right\}} + {{\alpha(2)}{ceil}\left\{ \Delta \right\}}}}}{Else}{\text{~~}{\Delta = {{\left( {1 - {\alpha(2)}} \right){floor}\left\{ \frac{{\alpha(1)}{{PGR}(k)}}{100} \right\}} + {{\alpha(2)}{ceil}\left\{ \frac{{\alpha(1)}{{PGR}(k)}}{100} \right\}}}}}{End}{{{PGR}\left( {k + 1} \right)} = {{{PGR}(k)} + {\Delta \cdot {{sign}\left( {{NPGR} - {{PGR}(k)}} \right)}}}}{{{{PGR}\left( {k + 1} \right)} = {{quant}\left( {{{PGR}\left( {k + 1} \right)},{ZZ}} \right)}};{{Quantize}{correction}{to}}}\text{}{{steps}{of}{{{ZZ}\left\lbrack {{mg}/{dL}} \right\rbrack}.}}$

More particularly, the new PGR (“NPGR”) is a function of a predefinedvalue (e.g., 1700) divided by twice the patient's total daily dosage oflong-acting insulin in the present insulin dosage regimen. In theforegoing formulas, the value of this divisor is represented by E{DT},since the value representing twice the patient's daily dosage oflong-acting insulin in the present insulin dosage regimen is substitutedas an approximation for the mean of the total daily dosage of insulinadministered to the patient (which data may, optionally, be employed ifthey are input into the memory by an insulin pump, such as in theexemplary apparatus described above, or by the patient using data entrymeans). The resultant value is subtracted from the present patient PGR(“PGR(k)”) to define a difference (“Δ”). If the Δ divided by the presentPGR(k) is less than or equal to the value of α(1) divided by 100, thenthe integer value of Δ (by which new PGR (i.e., PGR(k+1)) is updated) isa function of the formula Δ=(1−α(2))floor{Δ}+α(2)ceil{Δ}, where α(2) isthe insulin sensitivity correction factor (1 or 0), “floor” is value ofΔ rounded down to the next integer, and “ceil” is the value of Δ roundedup to the next integer. If, on the other hand, the Δ divided by thepresent PGR(k) is greater than the value of α(1) divided by 100, thenthe integer value of Δ is a function of the formula

${\Delta = {{\left( {1 - {\alpha(2)}} \right){floor}\left\{ \frac{{\alpha(1)}{{PGR}(k)}}{100} \right\}} + {{\alpha(2)}{ceil}\left\{ \frac{{\alpha(1)}{{PGR}(k)}}{100} \right\}}}},$where α(2) is the insulin sensitivity correction factor (1 or 0), α(1)is the percent value of the insulin sensitivity correction factor,PGR(k) is the present PGR, “floor” is value of Δ rounded down to thenext integer, and “ceil” is the value of Δ rounded up to the nextinteger. According to either outcome, the new PGR (PGR(k+1)) is equal tothe present PGR (PGR(k)) plus Δ times the sign of the difference,positive or negative, of NPGR minus PGR(k).

Furthermore, it is contemplated that the new PGR will be quantized topredefined steps of mg/dL. This is represented by the exemplary formula:PGR(k+1)=quant(PGR(k+1), ZZ) PGR(k+1)=quant(PGR(k+1), ZZ); where, by wayof a non-limiting example, ZZ may equal 5.

Also according to the update step 500, updates to the patient'sshort-acting insulin dosage regimen are undertaken by modifying thecarbohydrate ratio (CHR). CHR represents the average carbohydrate toinsulin ratio that a patient needs to determine the correct dose ofinsulin to inject before each meal. This process may be characterized bythe following, exemplary formulas:

${{{Calculate}{new}{{CHR}\left( {``{NCHR}"} \right)}:{NCHR}} = \frac{500}{E\left\{ {DT} \right\}}}{{{Calculate}{difference}},{\Delta = {❘{{{CHR}(k)} - {NCHR}}❘}}}{{{If}\frac{\Delta}{{CHR}(k)}} \leq \frac{\alpha(1)}{100}}{\Delta = {{\left( {1 - {\alpha(2)}} \right){floor}\left\{ \Delta \right\}} + {{\alpha(2)}{ceil}\left\{ \Delta \right\}}}}{Else}{\Delta = {{\left( {1 - {\alpha(2)}} \right){floor}\left\{ \frac{{\alpha(1)}{{CHR}(k)}}{100} \right\}} + {{\alpha(2)}{ceil}\left\{ \frac{{\alpha(1)}{{CHR}(k)}}{100} \right\}}}}{End}{{{CHR}\left( {k + 1} \right)} = {{{CHR}(k)} + {\Delta \cdot {{sign}\left( {{NCHR} - {{CHR}(k)}} \right)}}}}$

More particularly, the new CHR (“NCHR”) is a function of a predefinedvalue (e.g., 500) divided by twice the patient's total daily dosage oflong-acting insulin in the present insulin dosage regimen. In theforegoing formulas, the value of this divisor is represented by E{DT},since the value representing twice the patient's daily dosage oflong-acting insulin in the present insulin dosage regimen is substitutedas an approximation for the mean of the total daily dosage of insulinadministered to the patient (which data may, optionally, be employed ifthey are input into the memory by an insulin pump, such as in theexemplary apparatus described above, or by the patient using data entrymeans). The resultant value is subtracted from the present patient CHR(“CHR(k)”) to define a difference (“Δ”). If the Δ divided by the presentCHR(k) is less than or equal to the value of α(1) divided by 100, thenthe integer value of Δ (by which new CHR (i.e., CHR(k+1)) is updated) isa function of the formula Δ=(1−α(2))floor{Δ}+α(2)ceil{Δ}, where a(2) isthe insulin sensitivity correction factor (1 or 0), “floor” is value ofA rounded down to the next integer, and “ceil” is the value of A roundedup to the next integer. If, on the other hand, the A divided by thepresent CHR(k) is greater than the value of a(1) divided by 100, thenthe integer value of A is a function of the formula

${\Delta = {{\left( {1 - {\alpha(2)}} \right){floor}\left\{ \frac{{\alpha(1)}{{CHR}(k)}}{100} \right\}} + {{\alpha(2)}{ceil}\left\{ \frac{{\alpha(1)}{{CHR}(k)}}{100} \right\}}}},$

where α(2) is the insulin sensitivity correction factor (1 or 0), α(1)is the percent value of the insulin sensitivity correction factor,CHR(k) is the present CHR, “floor” is value of Δ rounded down to thenext integer, and “ceil” is the value of Δ rounded up to the nextinteger. According to either outcome, the new CHR (CHR(k+1)) is equal tothe present CHR (CHR(k)) plus Δ times the sign of the difference,positive or negative, of NCHR minus CHR(k).

As patients may respond differently to doses of short-acting insulindepending upon the time of day the injection is made, a different doseof insulin may be required to compensate for a similar amount ofcarbohydrates consumed for breakfast, lunch, or dinner. For example, onemay administer ‘1’ insulin unit for every ‘10’ grams of carbohydratesconsumed at lunch while administering ‘1’ insulin unit for every ‘8’grams of carbohydrates consumed at dinner. In the exemplary embodimentof the algorithm, this flexibility is achieved by the parameter Delta,δ, which is also updated in the step 500. It will be understood that thecarbohydrate to insulin ratio (CHR) as calculated above is the same forall meals. However, the actual dosage differs among meals (i.e.,breakfast, lunch, dinner) and equals CHR-δ. Therefore, the exemplaryalgorithm allows the dosage to be made more effective by slightlyaltering the CHR with δ to compensate for a patient's individualresponse to insulin at different times of the day.

Delta δ is a set of integers representing grams of carbohydrates, and ismore specifically defined as the set of values [δb, δl, δd], where “b”represents breakfast, “l” represents lunch, and “d” represents dinner.Delta, δ, may be either positive—thus reflecting that before a certainmeal it is desired to increase the insulin dose—or negative—thusreflecting that due to hypoglycemic events during the day it is desiredto decrease the insulin dose for a given meal.

Initially, it is contemplated that each δ in the set [δb, δl, δd] may bedefined by the patient's healthcare professional or constitute apredefined value (e.g., δ=[0, 0, 0] for each of [b, l, d], or [δb, δl,δd], thus reflecting that the patient's CHR is used with no alterationfor breakfast, lunch, or dinner).

The range of δ (“Rδ”) is defined as the maximum of three differences,expressed as max(|δb−δl|, |δb−δl|, |δd−δl|). In addition the algorithmdefines the minimal entry (“δ_(min)”) of the set [δb, δl, δd], expressedas min(δb, δl, δd).

Any correction to the patient's CHR can only result in a new Rδ(“Rδ(k+1)”) that is less than or equal to the greatest of the range ofthe present set of δ (Rδ(k)) or a predefined limit (D), which may, forinstance, be 2, as in the exemplary embodiment.

Against the foregoing, if the number of hypoglycemic events (HG) in agiven meal (b, l or d) over a predefined period (for example, 7 days) isequal to a predefined value (for instance, 2), and if the correspondingδb, δl, or δd is not equal to the δ_(min) or the range is 0 (R_(δ)=0),then the decrease in that δ (δb, δl, or δd) is equal to the presentvalue for that δ minus a predefined value (“d”), which may, forinstance, be 1; thus, δ_({i})=δ_({i})−d.

Otherwise, if the corresponding δ b, δ l, or δ d is equal to theδ.sub.min and the range is other than 0, then the decrease in that δ(e.g., δ b, δ l, or δ d) is effected by decreasing each δ in the set(i.e., [δ b, δ l, or δ d]) by the predefined value “d” (e.g., 1); thus,δ=δ−d (where δ refers to the entire set [δ b, δ l, or δ d]).

If, on the other hand, the number of hypoglycemic events stored in thememory is insignificant, it may be necessary to increase Δ in one ormore of the set (i.e., [δ b, δ l, or δ d]). To determine if an increaseis due, the algorithm looks for an unbalanced response to insulinbetween the three meals (b, l, d). A patient's response to his/herrecent short-acting insulin dosage is considered unbalanced if the meanblood-glucose-level measurements associated with two of the three mealsfalls within a predefined acceptable range (e.g., >α₁ but <α₂; where,for instance, α₁=80 and α₂=120), while the mean of theblood-glucose-level measurements associated with the third meal fallsabove the predefined acceptable range.

If the mean for two meals falls within [α₁, α₂], while the mean of thethird meal is >α₂, then the δ values for the updated set [δ b, δ l, or δd] are defined by the following, exemplary formulas:δ_(tmp)=δ;δ_(tmp)(i)=δ_(tmp(i)+d;)If (R_(δ-tmp)<=R_(δ)) or (R_(δ-tmp)<=D), then δ=δ_(tmp)

According to the foregoing, a test set of [δ b, δ l, or δ d], designatedδ_(tmp), is defined, wherein the value of each of δ b, δ l, and δ dequals the present value of each corresponding δ b, δ l, and δ d. The δvalue in the test set corresponding to the meal (b, l, or d) where theblood-glucose-level measurement was determined to exceed the predefinedacceptable range (e.g., >α₂) is then increased by the value “d” (e.g.,1), and the new set is accepted if it complies with one of thestatements: R_(δ-tmp)<=R δ (i.e., is the range R_(δ) of the test set(“R_(δ-tmp)”) less than or equal to the range (R_(δ)) of the presentset; or R_(δ-tmp)<=D (i.e., is the range R.sub.Δ of the test set(“R_(δ-tmp)”) less than or equal to the predefined value “D” (e.g., 2).

The foregoing will thus yield an increase in the insulin dosage for aparticular meal if the patient's mean blood-glucose-level measurementdata are outside of a predetermined range, such as, by way of exampleonly, between α₁=80 and α₂=120.

Further according to this step 500, the binary hypoglycemiccorrection-flag is reset to 0, reflecting that the patient's insulindosage regimen has been updated (and thus may be updated again at thenext evaluation).

It will be appreciated that the PGR and CHR values determined at step500 may optionally be employed by the processor to calculate, perconventional formulas, a “sliding scale”-type insulin dosage regimen.Such calculations may employ as a basis therefore a predefined averagenumber of carbohydrates for each meal. Alternatively, data correspondingto such information may be input into the memory by the patient usingdata entry means.

Per the exemplary algorithm as described above, it will be appreciatedthat if a hypoglycemic event causes some dosage reduction, no otherdosage can go up at the next update cycle, with respect to certainembodiments.

It should be noted that, according to certain exemplary embodiments ofthe algorithm herein described, any time a periodic evaluation of thepatient insulin dosage regimen is undertaken, the algorithm treats theinsulin dosage regimen as having been updated even if there has been nochange made to the immediately preceding insulin dosage regimen. And,moreover, any time the insulin dosage regimen is updated, whether inconsequence of a periodic update evaluation or an asynchronous update,the timer counting to the next periodic update evaluation will be resetto zero.

As noted, in operation of certain embodiments, there is initiallyspecified by a healthcare professional a patient insulin dosage regimencomprised of, for example, a long-acting insulin dose component, acarbohydrate ratio component and a plasma-glucose correction factorcomponent. This insulin dosage regimen data is entered in the memory ofan apparatus, such as by a healthcare professional, in the firstinstance and before the patient has made any use of the apparatus.Optionally, and as necessary, the internal clock of the apparatus is setfor the correct time for the time zone where the patient resides so thatthe time tags assigned to patient's blood-glucose-level measurements asthey are subsequently input into the apparatus are accurate in relationto when, in fact, the data are input (whether automatically, manually,or a combination of both). Thereafter, the patient will input, or therewill otherwise automatically be input (such as by the glucose meter)into the memory at least data corresponding to each successive one ofthe patient's blood-glucose-level measurements. Upon the input of suchdata, the processor determines, such as via the algorithm describedhereinabove, whether and by how much to vary the patient's presentinsulin dosage regimen. Information corresponding to this presentinsulin dosage regimen is then provided to the patient so that he/shemay adjust the amount of insulin they administer.

While the present disclosure has been described in connection withcertain embodiments, it is to be understood that the present disclosureis not to be limited to the disclosed embodiments, but on the contrary,is intended to cover various modifications and equivalent arrangements.Also, the various embodiments described herein may be implemented inconjunction with other embodiments, e.g., aspects of one embodiment maybe combined with aspects of another embodiment to realize yet otherembodiments. Further, each independent feature or component of any givenassembly may constitute an additional embodiment.

What is claimed is:
 1. A method for treating diabetes mellitus byalleviating glucotoxicity and/or restoring pancreatic beta-cell functionin a patient, the method comprising: storing one or more components ofthe patient's insulin dosage regimen; drawing blood samples from thepatient at a plurality of times; ascertaining the patient's bloodglucose-level measurements in said blood samples; tagging each of saidblood glucose-level measurements with an identifier reflective of whensaid measurement was obtained; establishing the patient's currentglycemic state relative to a desired glycemic range; determining fromthe patient's blood glucose-level measurements if there have been anexcessive number of hypoglycemic events over a predefined period oftime, and to vary at least one of the one or more components in thepatient's present insulin dosage regimen in response to a determinationthat there have been an excessive number of hypoglycemic events over thepredefined period of time; determining from the patient's bloodglucose-level measurements whether and by how much to adjust at leastone component of the one or more components of the patient's presentinsulin dosage regimen to stay within said desired glycemic range andadminister the lowest insulin dosage; adjusting the patient's insulindosage regimen in accordance with said determination of whether toadjust said insulin dosage regimen by increasing, decreasing, or pausingthe patient's dosage regimen to dampen or prevent unstable oscillations,wherein said adjustment weighs both A1C percentage and the number ofhypoglycemic events within the predefined period of time in order toachieve an improved glycemic composite index, and the restoration ofbeta-cell function which reduces exogenous insulin reliance; andperforming said steps of the method until the insulin dosage regimenrequired to stay within the desired glycemic range is lowered to apredetermined level.
 2. An apparatus for alleviating glucotoxicityand/or restoring pancreatic beta-cell function in a patient over time,comprising: at least a first computer-readable memory for storing one ormore components in a patient's present insulin dosage regimen and thepatient's blood-glucose-level measurements determined at a plurality oftimes within a predefined period of time; at least one data input forobtaining data corresponding to the patient's blood glucose-levelmeasurements determined at the plurality of times; a timer formonitoring the predetermined time period, the timer being incrementedbased on at least one of the passage of a predetermined increment oftime and the receipt of at least one of the plurality of bloodglucose-level measurements; at least one processor operatively connectedto the at least first computer-readable memory, the processor programmedat least to: tag the plurality of blood glucose-level measurements withan identifier reflective of when the measurement was obtained;calculate, after obtaining one of the plurality of blood glucose-levelmeasurements but before obtaining a subsequent blood glucose-levelmeasurement, any deviation of the obtained blood glucose-levelmeasurement from a desired glycemic range; adjust at least one of theone or more components in the patient's insulin dosage regimen inresponse to a determination that the most recently obtained bloodglucose-level measurement was not within a desired glycemic range;wherein the timer is reinitiated after the determination that the mostrecently obtained blood glucose-level measurement was not within adesired glycemic range over the predefined period of time; determine, atthe end of the predefined period of time, from a plurality of the datacorresponding to the patient's blood glucose-level measurements, thevariation of at least one of the one or more components in the patient'sinsulin dosage regimen that is necessary in order to maintain thepatient's blood glucose-level measurements within a predefined range;reinitiate said timer after said adjustment and said determination; andrepeatedly tag said measurements, calculate said deviation, adjust saidat least one component, and determine said variation until the insulindosage regimen required to stay within the desired glycemic range islowered to a predetermined level.
 3. A system for alleviatingglucotoxicity and/or restoring pancreatic beta-cell function in apatient, the system comprising: at least one memory for storing datacorresponding at least to one or more components of a patient's presentinsulin dosage regimen, and data corresponding at least to the patient'sblood-glucose-level measurements measured at a plurality of times duringa predetermined time period; and at least one processor operativelyconnected to the at least one memory, the at least one processorconfigured to: initiate a timer to monitor the predetermined timeperiod; increment the timer based on at least one of the passage of apredetermined increment of time and the receipt of at least one of theplurality of blood glucose-level measurements; tag the plurality ofblood glucose-level measurements with an identifier reflective of whenthe measurement was obtained; calculate, after obtaining one of theplurality of blood glucose-level measurements but before obtaining asubsequent blood glucose-level measurement, any deviation of theobtained blood glucose-level measurement from a desired glycemic range;adjust at least one of the one or more components in the patient'sinsulin dosage regimen in response to the determination that the mostrecently obtained blood glucose-level measurement was not within adesired glycemic range; wherein the timer is reinitiated after thedetermination that was not within a desired glycemic range; anddetermine, at the end of the predetermined time period, from a pluralityof the data corresponding to the patient's blood glucose-levelmeasurements whether and by how much to vary at least one of the one ormore components in the patient's present insulin dosage regimen in orderto maintain the patient's blood glucose level measurements within apredefined range; wherein the timer is reinitiated after thedetermination of whether and by how much to vary at least one of the oneor more components in the patient's present insulin dosage regimen; andrepeatedly increment said timer, tag said measurements, calculate saiddeviation, adjust said at least one component, and determine saidvariation until the insulin dosage regimen required to stay within thedesired glycemic range is lowered to a predetermined level.
 4. Thesystem of claim 3, wherein the data corresponding at least to thepatient's blood-glucose-level measurements determined at a plurality oftimes are associated with an identifier indicative of when themeasurement was input into the memory.
 5. The system of claim 4 whereinthe one or more components in the patient's present insulin dosageregimen comprise a long-acting insulin dosage component, and wherein theprocessor is programmed to determine from the identifier indicative ofwhen a measurement was input into the memory at least whether themeasurement is a morning or bed-time blood-glucose-level measurement, todetermine whether the patient's morning and bed-time blood-glucose-levelmeasurements fall within a predefined range, and to determine by howmuch to vary the patient's long-acting insulin dosage component onlywhen the patient's morning and bed-time blood-glucose-level measurementsare determined to fall outside of said desired glycemic range.
 6. Thesystem of claim 5, wherein, in connection with the determination of byhow much to vary at least one of the one or more components in thepatient's present insulin dosage regimen, the at least one processor isprogrammed to factor in an insulin sensitivity correction factor thatdefines both the percentage by which any of the one or more componentsof the insulin dosage regimen may be varied and the direction in whichany fractional variations in any of the one or more components arerounded to the nearest whole number.
 7. The system of claim 6, whereinthe at least one memory further stores data corresponding to a patient'spresent weight, and wherein the insulin sensitivity correction factor isin part determined from the patient's present weight.
 8. The system ofclaim 6, wherein the determination of by how much to vary thelong-acting insulin dosage component of a patient's present insulindosage regimen is a function of the present long-acting insulin dosage,the insulin sensitivity correction factor, and the patient'sblood-glucose-level measurements.
 9. The system of claim 3, wherein theprocessor is programmed to determine on a predefined schedule whetherand by how much to vary at least one of the one or more components inthe patient's present insulin dosage regimen.
 10. The system of claim 3,wherein the processor is programmed to determine from the data inputscorresponding at least to the patient's blood-glucose-level measurementsdetermined at a plurality of times if the patient's blood-glucose levelmeasurements fall within or outside said desired glycemic range, and tovary at least one of the one or more components in the patient's presentinsulin dosage regimen only if the patient's blood-glucose levelmeasurements fall outside of said desired glycemic range.
 11. The systemof claim 10, wherein the processor is further programmed to determinefrom the data inputs corresponding at least to the patient's bloodglucose-level measurements determined at a plurality of times whetherthe patient's blood glucose-level measurements determined at a pluralityof times represent a normal or abnormal distribution.
 12. The system ofclaim 10, wherein the determination of whether the patient'sblood-glucose-level measurements determined at a plurality of timesrepresent a normal or abnormal distribution comprises determiningwhether the third moment of the distribution of the patient'sblood-glucose-level measurements determined at a plurality of times fallwithin said desired glycemic range.
 13. The system of claim 3, whereinthe one or more components in the patient's present insulin dosageregimen comprise a short-acting insulin dosage component defined by acarbohydrate ratio and plasma glucose correction factor, and wherein theprocessor is programmed to determine whether and by how much to vary thepatient's carbohydrate ratio and plasma glucose correction factor. 14.The system of claim 13, wherein, in connection with the determination ofby how much to vary at least one of the one or more components in thepatient's present insulin dosage regimen, the at least one processor isprogrammed to factor in an insulin sensitivity correction factor thatdefines both the percentage by which any one or more components of theinsulin dosage regimen may be varied and the direction in which anyfractional variations in the one or more components are rounded to thenearest whole number.
 15. The system of claim 14, wherein thedetermination of by how much to vary the present plasma glucosecorrection factor component of a patient's insulin dosage regimen is afunction of a predefined value divided by the mean of the total dailydosage of insulin administered to the patient, the patient's presentplasma glucose correction factor, and the insulin sensitivity correctionfactor.
 16. The system of claim 15, wherein a value representing twicethe patient's daily dosage of long-acting insulin in the present insulindosage regimen is substituted for the mean of the total daily dosage ofinsulin administered to the patient as an approximation thereof.
 17. Thesystem of claim 15, wherein the plasma glucose correction factorcomponent of the patient's insulin dosage regimen is quantized topredefined steps of mg/dl.
 18. The system of claim 14, wherein thedetermination of by how much to vary the present carbohydrate ratiocomponent of a patient's insulin dosage regimen is a function of apredefined value divided by the mean of the total daily dosage ofinsulin administered to the patient, the patient's present carbohydrateratio, and the insulin sensitivity correction factor.
 19. The system ofclaim 18, wherein a value representing twice the patient's daily dosageof long-acting insulin in the present insulin dosage regimen issubstituted for the mean of the total daily dosage of insulinadministered to the patient as an approximation thereof.
 20. The systemof claim 18, wherein the at least one processor is programmed todetermine a correction factor that allows variations to the carbohydrateratio component of a patient's insulin dosage regimen to be altered inorder to compensate for a patient's individual response to insulin atdifferent times of the day.